{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dfdfa2e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T13:23:04.701981Z",
     "iopub.status.busy": "2024-04-02T13:23:04.701562Z",
     "iopub.status.idle": "2024-04-02T13:23:08.133833Z",
     "shell.execute_reply": "2024-04-02T13:23:08.132625Z"
    },
    "papermill": {
     "duration": 3.446596,
     "end_time": "2024-04-02T13:23:08.136611",
     "exception": false,
     "start_time": "2024-04-02T13:23:04.690015",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pylab as plt\n",
    "from scipy.stats import ks_2samp\n",
    "\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.inspection import permutation_importance\n",
    "from sklearn.linear_model import Ridge, LinearRegression\n",
    "from sklearn.model_selection import cross_val_score, train_test_split, ShuffleSplit\n",
    "\n",
    "import skopt\n",
    "\n",
    "from tspiral.model_selection import TemporalSplit\n",
    "from tspiral.forecasting import ForecastingCascade, ForecastingChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68038443",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T13:23:08.157942Z",
     "iopub.status.busy": "2024-04-02T13:23:08.157386Z",
     "iopub.status.idle": "2024-04-02T13:23:08.291607Z",
     "shell.execute_reply": "2024-04-02T13:23:08.289931Z"
    },
    "papermill": {
     "duration": 0.149461,
     "end_time": "2024-04-02T13:23:08.295884",
     "exception": false,
     "start_time": "2024-04-02T13:23:08.146423",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### UTILITY FUNCTIONS TO GENERATE SYNTHETIC DATA ###\n",
    "\n",
    "def gen_sinusoidal(n, amp, freq, noise):\n",
    "    X = np.arange(n)\n",
    "    e = np.random.normal(0,noise, n)\n",
    "    return amp*np.sin(X*(2*np.pi/freq))+e\n",
    "\n",
    "def gen_randomwalk(n, noise):\n",
    "    return np.random.normal(0,noise, n).cumsum()\n",
    "\n",
    "def gen_ts(n, random_state=0):\n",
    "    np.random.seed(random_state)\n",
    "    seas1 = gen_sinusoidal(n=n, amp=10, freq=24*4, noise=4)\n",
    "    seas2 = gen_sinusoidal(n=n, amp=10, freq=24*7*4, noise=4)\n",
    "    rw = gen_randomwalk(n=n, noise=1)\n",
    "    X = np.power(np.linspace(0,10, n).reshape(-1,1), [1,2])\n",
    "    trend = LinearRegression().fit(X, rw).predict(X)\n",
    "    return seas1 + seas2 + trend\n",
    "    \n",
    "\n",
    "n_series, n = 10, 6_000\n",
    "df = pd.DataFrame(\n",
    "    {f'ts_{i}': gen_ts(n=n, random_state=i)\n",
    "     for i in range(n_series)},\n",
    "    index=pd.date_range('2010-01-01', periods=n, \n",
    "                        freq='1h', name='ts')\n",
    ")  # df.shape: (n_series, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7864adf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T13:23:08.337767Z",
     "iopub.status.busy": "2024-04-02T13:23:08.336997Z",
     "iopub.status.idle": "2024-04-02T13:23:10.831592Z",
     "shell.execute_reply": "2024-04-02T13:23:10.830598Z"
    },
    "papermill": {
     "duration": 2.521737,
     "end_time": "2024-04-02T13:23:10.837137",
     "exception": false,
     "start_time": "2024-04-02T13:23:08.315400",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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mPIFJj0/CC2+9AEBeCMhb413n/cvdB5NGNWa2b9/Oev3LL78gNDQUCQkJGDBgAMrKyvDzzz9j1apVGDx4MABg+fLlaN++PY4ePYrevXtjx44duHjxInbt2oWwsDB06dIF77zzDl566SW8+eab0Gj4CUgEAoFAIMjBbDWj3FgOX43N05BXlSc5P7cqV3A5s+zZobdCUbZQj8aLNbfKWMoKAdUoldCp5AedhMJHVpUHaLUOtFnedjzVngCAYM9gqDVqWC3OqiylQomQwBBMnz4dPcb0wJrea/Dpm586jBlXaFVa+Gn9ZM0V45YKM3EpK7MlVQUGBgIAEhISYDKZMHToUMecdu3aoVmzZoiPjwcAxMfHo1OnTggLC3PMGTFiBMrLy5GYmCi4H4PBgPLyctaDQCAQCAQuB7MOou8ffR1y+gpFw18WuWJyXG/QtdJr9d5HtbkaRTXyy7KjfaMBACG6EDSJaoJzp84hKz0LJUUl+Onjn7Bx40YkJycj+XIy9u/YjxZtWsjabk5mDgquFiA9PR0WiwWXz1/G5fOXoa9svPwa4AYaM1arFfPmzUPfvn3RsWNHAEBubi40Gg38/f1Zc8PCwpCbm+uYwzRk7OP2MSHef/99+Pn5OR5RUfKFfwgEAoFw52Hvt3QiR6AqCa71ZKS4WHSRtb4rr0OpoRSpZanQm/RutSBwZ649jKOgFHh87uNQKpUY128c+rfrD41Gg0WLFiEuLg7T75sOpVKJj3/42LGuWiGuP/PVB1/h7rvuxhtvvAF9lR4PDn4QDw5+EBfOXmDNC9YFi2yhbtywaqY5c+bgwoULOHToUKPva9GiRViwYIHjdXl5OTFoCAQCgSCK3VuyLXWb4Piig4vw4YAPRde3GxJiRk+VqQo+Gp/aydLHklVha+p4vew6/LX+aOLDryaSQ4RXBHKqclzOa92mNVZuW+l43SawDZa8ZctJrTBWsNSGFZQCXmpb2MxL7cUT6Xvvq/ew6rdV8FR7IrFQOHoC2AT7fLW+DeKVAm6QZ2bu3LnYsmUL9u7di6ZNmzqWh4eHw2g0orS0lDU/Ly8P4eHhjjnc6ib7a/scLlqtFr6+vqwHgUAgEAh1Zev1rcipFDcM7G0N0srSJLdjsVrc6hDNbYfgjvclUBfoaFkgRVPvpqzXTM8L1wvTPqi9w7PkMM442Dt6t/JvJbpPiqLgoax7kjKXRjVmaJrG3LlzsWHDBuzZswfNmzdnjXfv3h1qtRq7d+92LEtKSkJ6ejr69LH1dOjTpw/Onz+P/HynnPTOnTvh6+uL2NjYxjx8AoFAIPwHERJ7E+rBxGX4uuGiY2arTS7ETAvLhpistoaTzL5OTKTCWHqTM9+EWQUlB7HeUUw8VB5oH9QeEV4RaBUgboD8uPRHeHt7sx69YnqhZ3RPzHp4lmOe3eDSqrSS+6UoStDYktuPiUmjhpnmzJmDVatWYePGjfDx8XHkuPj5+UGn08HPzw8zZ87EggULEBgYCF9fXzzzzDPo06cPevfuDQAYPnw4YmNjMXXqVHz00UfIzc3Fa6+9hjlz5kCrlf6gCAQCgUDgklmZyV/oXvEMj5n/zsSyLstEx3MqcxDoEShqzJTUlCBIFyQ4lqvPRQs/WwKuvaGjXOTm+igoBQJ1gZJznv2/ZzF3xlzWMpqmkVySDK3OeT2mGXG0lv4tkVKaIrrNUM9Q5JWxoy+fDfpM1jEzaVRj5ttvvwUADBo0iLV8+fLlmDFjBgDg888/h0KhwIQJE2AwGDBixAh88803jrlKpRJbtmzB7Nmz0adPH3h5eWH69Ol4++23G/PQCQQCgfAfRY63oi7UmGugquNl1X5MQl6j6toWBUwPjSvsIR45HicpVArn+2ke0VwwedlYKK6KzFy/MWnUvciJ7Xl4eODrr7/G119/LTonOjoaW7dubchDIxAIBEIjY6Wtbouf3Qis1rpXJklhoS11NmbsiIWpqkxVSC1Llb0dqRBPlI/8ghiVQoUW/i2ggEK29gvz2l9fY0out963jEAgEAi3Pduub0PnXzuj04pOklUtNwMhz0xdLrrDo9k5NFUmfkNJJnKMEakwVEOhVCjdmq9T6SSNo1DPULf2Za+GEmJQ00FuHZsdYswQCAQCocF58cCLjueT/pl0w/efU5mDZ/Y8gxO5fN2YanM1b1mNucbtfTTzbcZ6bU8CFkPK2HFV2s3t5C0FM7RzIzwjciqmmPh7+LNeM6uiFvYU74slBTFmCAQCgXDLcbbgLD48/iEqjZV1Wn/x4cXYl7EPj//7OGu53qTH1G1TefO/Pfut2/uoj5CeGEI5M/VBx2h94Kv1hYfKA54qzwbdBxeaI6TDM3Y4GShapdPro6Tc8xrZuWGieQQCgUC4M7hScqXe23h066MAbAbDol6L3F5frIT5dP5pweWp5amifZfE8FZLN1P00/q55VEBXIeq5MD0xvhp/EB70/BUeUKr0oKmabf7HtUXbt8ptZKtXcP8HKVCUFIQzwyBQCAQGpTXDr3GWyZUEGKymHAm/4xkeOZaWcMoxNqRSkge9pd7+ibjW4+XHG/iLV+5l+vNkINoeIdhq1AUhQCPAEfOy402ZACwPEGR3pE8g0WtVMNP64dZnWchwCOgTvsgxgyBQCAQGhSjhV+quzt9N2/ZG0fewNRtU7E0YWmDH4OYh6OuYQwhxHRh7LgyHJjj3LYAcgj1DBVMsA3zDBOYLc6gQYMwb948t/fPpJlvM2SlZ+GNeW+gS7su0Ol0aNmyJd544w1YzBa0C2yH9kHtRY0VL7UXRrcYXef9E2OGQCAQCG7z8YmPce+Ge3Gt9JqsSpvMCr5Q3eZrmwEAKy6uEF2vLh6L4ppiVsuAXxN/dTx3p1T8jT5vuL1vO55q13kpYu0A3IGZb2JHo9TUe7vu4qPxgTXfCh+1D77//nskJibi888/x3fffYdXXnkFSoWyUcv0iTFDIBAIBLf59eKvSCtPw7iN4zDgzwGSfYuESC5JbqQjA45mH2W9/viks+OzO2XJD7Z5sM7HIMeY4WKymNxeJ9I70u11mMyYMQP79+/HsmXLQFEUKIrC6dOnMWXKFISEhECn06F169ZYvny5y22NHDkSy5cvx/Dhw9GiRQvcd999WLhwIdavX1+vY5QDSQAmEAgEQr357dJveLGnrRy73FjOG+d6WKZsnXJDjotLQ5cqv9nnTbwZ/yZveZBHEGializ51ig1rPFcfa5bJeJ2RWAFpWCpA1ebq+Gh9JCVH7Ns2TJcuXIFHTt2dCjrv/XWW7h48SK2bduG4OBgJCcno7qaX84uh7KyMgQGSrdJaAiIMUMgEAiEevPbRacxU2nil1N/lvAZpsVOc3hG9GZ50vzuVgNJkVKaIliWXR/GtBgjaMxQoFBtrsaMf2c06P7kcmzyMVneIT8/P2g0Gnh6eiI8PBwAkJWVha5du6JHjx4AgJiYmDodQ3JyMr788kt88skndVrfHUiYiUAgEAhu4UpfRUUJ3yfn6/NF1xEri75cfBnbU7fLPziIJ96+Hd9wPf2W9FsCwNZx2p1juB2YPXs2Vq9ejS5duuDFF1/EkSNH3N5GVlYWRo4ciYceeghPPvlkIxwlG+KZIRAIBIJbCHlemFghbOzY2wgIlWJLhVde2P8CRsaMdOMI+WRUZDSYyN2w6GEY23Ks43W7wHYoq+R7kHQqHX4Z8YvgNkK9QlFjrkG5gR+Ss9MqoJVkblH7oPaO55eKLjmeN/dvztN2cYdRo0YhLS0NW7duxc6dOzFkyBDMmTNHtoclOzsb99xzD+6++2788MMPdT4OdyCeGQKBQCCIUmmsxPbr21k5Ga5k+8WMBrvWzMcnPuaNMSX469JawBVbUrbwlj0d97TL9Rb3XuxyzqcDP+Uto2BLpvVQeQg+fNQ+8Nf6i457qDzgp/WTHPdUezoe3hpvx3J/rb9bniGNRgOLha08HBISgunTp+P333/H0qVLZRslWVlZGDRoELp3747ly5dDobgxZgYxZggEAoEgygsHXsALB17AW/FvOZa5ktwXG7d7ZlZdXsUbU1JKmCwmVBorMfDPgbKPL7syGwaLgbWMWZZt55uz3/Au8AObut7PxLYTXc7h9mhiolaoBZfToF3q1LhDjF8MAj0C0Tqgtdsl0DExMTh27BhSU1NRWFiI119/HRs3bkRycjISExOxZcsWtG/f3uV27IZMs2bN8Mknn6CgoAC5ubnIzXVPWbkuEGOGQCAQCIKUGcpwKOsQAGDr9a2O5UJdp5kYrXzRPMAWfhJSAraPjd4wGn3+6COYHJxVmcV6vezUMnRa0Qkj1o1Aj997oEBfAMBW4fPB8Q+E98HxGLUKaCX6Hj4ZKB5SkVMRZTecmvo0FRz3Vns3qO6KVqlFhHdEnTRmFi5cCKVSidjYWISEhECj0WDRokWIi4vDgAEDoFQqsXr1apfb2blzJ5KTk7F79240bdoUERERjkdjQ3JmCAQCgSBIv9X9BJdLhZmkqo+sVivLw8Mao62SvZGKq4sd7QH0Jj1+Ov8Ta3zw2sE4O+0s/kz6U3Qb3G7ZUnklI2JGiI65g5Bx4a/15/UncgcFpUCwLrg+h8WiTZs2iI+PZy177TV+SwpXzJgxAzNmzGigo3IP4pkhEAgEAg+uJ4TJidwTgsu/OPWFqAEEAHn6PKy7uk5w7Luz38k6rmpzNXqt6iU4VmGsQIWxQnRdoQaYAdq69QKqD0185PdsEqJdYDvxvkx3KMSYIRAIBAKP0euF++RYrBa8fuR1wbEfz/8ouc1Zu2aJjm1K2SS5rl10L6U0RXKeXP0aO98N4xtRrrphu0qujfGLcTxXUso69YOK9o0GALQJbOP2/huLJUuWwNvbW/AxatSom3JMdkiYiUAgEAg8xCqSTFb3JfcbAntoy1W+Smp5qlvbjQ2KRVPvpsisdPaOmtx+stvHZ+9KDYDVFZqiKLQJbOMonY7yiXK5rWBdMLw1NoNKLIH4ZjBr1ixMnCicEK3T1b0UvCEgxgyBQCDcRHKrcpFWnoZeEcKhk1sJmqbx0oGXbsq+f734K7qFdXPplXBVNm6nbUBbx/NND2xCt9+6OV7P7jxbcl0hg0ossRlgN7eU46Vxp3/UjSQwMPCGtCaoCyTMRCAQCDeRYX8NwxM7nsDRnKOuJ99kjmQfwZ6MPTdl37vTdwNw3fW60igt6Gfnh+FO3RS1Qo33+7/veM3UvGlwZESIAj1uTYPhVoYYMwQCgXALcDL35M0+BJeUGkpv9iG4DDMlFiXK2g7XYBgWPQxxwXF4rMNjvLlc4TxmToy7cMNG3HYIgR6BkgabWKn3nQ4xZggEAuEWQEjozRWvH34dU/6ZIju0Ihem2i8TIX2ZII+6C78Nix7m1vz9Gfvdmv9O33dkz9UqtVg5ZiUW9FjAG+MK5z3e8XHeHG5XcC4t/Fsg2jeaV6od5RMFf62/7OP00/rJnnsnQYwZAoFAuAVYe2Wt2+tsSN6Ac4XncDz3eIMei1jp89/Jf/OWaZVa/sRamvmIK+MCwN2Rd7t1XHP3zMWDmx8UHU8qTmK9bh/YHkvvWerWPuQgpE8T6R3pch17Ui8TjVKDCG+nqFxDCundSZBPjUAgEBoRqcRQVx4VqXWZ1LWBoslqwqxds7Azbaes+WllabxlUgm541qNk9zeA60ekLVfuczcMZO3bEizIQ26DzFeuesVeKg8EOXtulqJi4JSOD7HhmxxcCdBjBkCgUBoJC4VXcKQtUMEPRoAcN/f94muW2GswIh1I/B2/Nsu91NXY2bImiE4nHUYC/bxQytCmGm+8SVlcHUJ6SK5vVu1akeISC9pz0uEdwQCPQLhofaQnCdGbFAsYoNiBZOP7W0XwrzC6rTtOwFizBAIBEIj8eKBF1FQXYDFh/mdl0/mnkRGRYbouptSNiGnKkdW+EmuB4dLiaHErflCeT1Smix3RdwlOjYqpv4ia6620ZDict8M/Qb9mvTDytErG2ybXMSOV6vUIjYotkFbGDAZNGgQ5s2b1yDbuu+++9CsWTN4eHggIiICU6dORXZ2doNsWwpizBAIBEIjUFhdKCrgllmRicf+5VfNMGEaKK7CQO54ZmiaxlM7nsKUf6bIXkeKabHTJMc/6C/c9PGjgR/Ve98fDfyIV2nERKe05bb8Nuo31vJJbSe5va+W/i3x7dBvERcS5/a6DcHNUv11l3vuuQdr1qxBUlIS1q1bh5SUFDz4oHieU0NBjBkCgUBoBJ749wnRMWYHajks2LcAvyb+KjpuBd+YKTOUYVPKJl5lUp4+D/E58ThXeI61/EDmAQDA6fzTbh2b2EX2q8FfAYCjOWRdiPGNcTlHSiE3yteWv8JNqnVlgN1JzJgxA/v378eyZctAURQoisLp06cxZcoUhISEQKfToXXr1li+fLms7c2fPx+9e/dGdHQ07r77brz88ss4evQoTKbGVY4mCsAEAoHQCKSUifcQ+uvKXy7XP5R9iPX645Mf456oexwXaCZCYabn9j6HhLwExLeIZwnCiTFn9xycn37eZY8kJkwVXS4DowYCcO1RiPaNRlo5P7EYAP6890+8d+w9t47JTp+IPo7n3LLp+nSsdgeapkFXV7ue2AhQOp0sb86yZctw5coVdOzYEW+/bcvPeuutt3Dx4kVs27YNwcHBSE5ORnUd3kdxcTFWrlyJu+++G2p1437mxJghEAiEG4wrhdmMigwczjrMW15uKhecLxRmSshLAABsubZFljFjR46hZceumTI8ejh2pO0QnNMxqCO6hHTBmYIzguNSInieak+X1T1iF+yuoV0dz7nGnlQ5eUNCV1cjqVv3G7IvLm1PJYDy9HQ5z8/PDxqNBp6enggPDwcAZGVloWvXrujRowcAICYmxq19v/TSS/jqq6+g1+vRu3dvbNmyxe3jdxcSZiIQCIQbwNmCs46Lqlh/ns9OfgYAOJJ1RHgjInm+5wvPS+57ReIKeQfpJvb8kTld54jOUSqU+G30b6LjrtAoNJLjYrosj3UUz0lq1HYF/wFmz56N1atXo0uXLnjxxRdx5IjI91GEF154AadPn8aOHTugVCoxbdq0Oiepy6VR/6MHDhzAxx9/jISEBOTk5GDDhg24//77HeMzZszAihXsH9mIESOwfft2x+vi4mI888wz2Lx5MxQKBSZMmIBly5bB21u6RTuBQCDcSjy69VF8PeRrDGg6QPRiujxxuaACrRDMho+/JP6C53s8Lzr3k5OfYHqH6e4dcC0zO87Ezxd+5i2P8IrAs12fBQC08GvhcjuT203Gqsur3N6/q/Jti5WvSkyB4rUJYOKtvjHXD0qnQ9tTCTdkX0L7riujRo1CWloatm7dip07d2LIkCGYM2cOPvnkE1nrBwcHIzg4GG3atEH79u0RFRWFo0ePok+fPq5XBlCmN+FCdhn6tJCvudOonpmqqip07twZX3/9teickSNHIicnx/H4448/WONTpkxBYmIidu7ciS1btuDAgQN46qmnGvOwCQQCoVHYkWoLxbjqnFxpEm6WyMz94CYRV5mq6nl0wjzX7TnB5Tse3AFPteswhh0x8TpXLQ1cGR5CvYq4y7g5MzeqMoiiKCg8PW/Kw533qNFoYLGwjcKQkBBMnz4dv//+O5YuXYoffvhBZG1prFZbCNRgMMhe596vDmLKT8fw50lx6QIujeqZGTVqFEaNktYB0Gq1jjgdl0uXLmH79u04ceKEI3b35ZdfYvTo0fjkk08QGSktYkQgEAiNydWSq1h9eTWeintKlqCZ/QLjSvl36amlbh/LX1f+kvS+ZFdmu5TcF6IxLvwjY0Y6ns/qPAtlhjKsubJGcO6DbR7ERyfEy7h7hPXgLeOGNBo7xHG7ExMTg2PHjiE1NRXe3t744osv0L17d3To0AEGgwFbtmxB+/btXW7n2LFjOHHiBPr164eAgACkpKRg8eLFaNmypWyvDABkFNuSjf85l4Mx7fxlrXPTc2b27duH0NBQtG3bFrNnz0ZRUZFjLD4+Hv7+/g5DBgCGDh0KhUKBY8eOiW7TYDCgvLyc9SAQCISGZvym8VhzZQ1ePPCirPl/J/+NMkOZZKVTXfnkpHQIQI7K7+aUzXXe/z8P/ON4LqT9wmyw+EqvV1jLF/eR0IpR6bDlAfEEUoqisPuh3axlfZv0Zb121QTyTsRsdSaNL1y4EEqlErGxsQgJCYFGo8GiRYsQFxeHAQMGQKlUYvXq1S636enpifXr12PIkCFo27YtZs6cibi4OOzfvx9abeMmXd/ULKiRI0di/PjxaN68OVJSUvDKK69g1KhRiI+Ph1KpRG5uLkJDQ1nrqFQqBAYGIjc3V3S777//Pt56663GPnwCgUAAAJzKP8V6fbHooujcfqv71Xk/9fEwJBYlupzzyqFXBJefmXoGXX7r4ng9syO/B1Iz32Y49ajtcxAqfY4LicOQZkPQzKcZAjwCeOM9w3viRO4Jwf1LackAQKgn+zqxoDvbcGsd0Fpy/TuNnNJqFFQaEB3kBT+dGm3atEF8fDxrzmuvveb2djt16oQ9e/Y01GG6xU01ZiZNcqowdurUCXFxcWjZsiX27duHIUPq3hxs0aJFWLDA+WUuLy9HVJT7zb8IBMLNpUBfgO/PfY+H2z58y12Qll8QFxHbm7G3ztuVY7C8eujVOm9/T7r7FxtuEm6gR6DgPCn9FgWlkOxg7a/1Z72e0HqC47m7vae4uTy+Gl/sm7gPX535Cr0jeru1rf8iBZW2/JWcsmr46dzXf7FYaeiNZnhpVVDcIsrENz3MxKRFixYOgR4ACA8PR35+PmuO2WxGcXGxaJ4NYMvD8fX1ZT0IBMLtx6KDi/Bn0p94cHPjy6G7y2cJn7FeM6tq/nf+f3XebrXZtThZXUTk7Lx/XL7mjBgPtmn4/wfXiHvpLme1VriX+PleLkG6ILzR5w2MiBlR723dqSxZsgTe3t7w9fFBWFAAfLx94O3tDW9vb5f5sY3NLVVsn5mZiaKiIkRERAAA+vTpg9LSUiQkJKB7d5vw0J49e2C1WtGrV6+beagEAuEGcLHYFq6pa1foG8nZgrPoFtYNAGC0Guu8HYNFvOpDqG2BEHlVeYLLz+Sf4S2L8omSbHjJJcY3xq0qJrlYaKcxOKntJOhUztJioVJ2oVAXoXGZNWsWJk6ciMs5zjzUdhE2Z4GuHqXgYrjj9GlUY6aystLhZQGA69ev48yZMwgMDERgYCDeeustTJgwAeHh4UhJScGLL76IVq1aYcQIm+Xcvn17jBw5Ek8++SS+++47mEwmzJ07F5MmTSKVTATCncBtlLeZWJToMGbqw4A/B4iOrUhcgc8GfSY6buf1I68LLp+6bSpvWfvA9rKMGa1SC4PFgO5hjaNoyzRYX+3tOow2r/s81utAj0DBrt6EhsN+7dZ7lDqWtWrq32j7O3i1EM/8ccr1RDRymOnkyZPo2rUruna1yUovWLAAXbt2xeuvvw6lUolz587hvvvuQ5s2bTBz5kx0794dBw8eZGU9r1y5Eu3atcOQIUMwevRo9OvXr8717gQC4fYluSTZ9aRGwGAx4GjOURgt0t4WqfLhhsJV92w7R7LdU2wVY9P9znDWhvs2YF63eXih5wsNsm0uz3R9BkpKicc7Pu5yboRXBG/Z8pHL0TuiN34Z+UsjHB3hZrH3coGseY3qmRk0aJBkMtu///7rchuBgYFYtcp91UgCgXD7wyypfWjLQzg91b2Ozg3BG0fewD/X/sGE1hPw5t1v2o7rJuqWdFrR6Ybtq7lfc8fzKN8ozOzUeKGdtoFtcWLKCVlNILeO53cdb+HXAj8O/7ExDu0/i9EsHrasNppxNb8S/p4aNAts+LBiQ3NLJQATCIQ7j18u/IKF+xcKytIzcSU011j8c82mn7Lu6jrnsdDCx3K15CovvydEFyK5fabBUF/ejn8bBzIPNMi2mNVENwopQ+bY5GP4fNDnSHg0gfRWamQMZguu5ttUqEv1Rpgt4kaP9RYRJCTGDIFAuKl8mvAp/k39F/sy9/HGasw1N/6AZCBmWI3fNB4bkzeylr1818uS2/py8JcNdlxrr6zFs3uedWsdMUE5oVDOzcRT7Ymh0UNZ4nuEhsdqpZGUW8FaZmQYM/kV7N9kXvmt8RslxgyBQLgl4JYkW2mrqAfETnZlNnak7miUaieapkVDOkXVRYLLAX7yLbMqR4ho32jJ8emx7jWIZFYFyWFg04HC+61jY0rC7U1eBd84sVidBm9uGXu8uKrulXsNCTFmCATCLcHR7KOs1zlVOZLz92Xsw4h1I/D8/uex/MJyXCu75hizWC0o0NsSB2vMNZiwaQLeO/qeW8cjpZjL1ZiRgiutL8So5uIaHc18m8neV10Y23Ks4HKprtOE/y56I98Yvl7YOE1MGxJizBAIhJsGM79jYwo7PPPWEemWJM/secbxfOmppRj39zgUVhcCAObumYvBawfjWM4x7EzbiSslV7A6Sbi3zNWSq3j10Ku88mSxztUAcL3suuSxMVFQrk+zUnL9Qo0UG4po32hZx0f478JNZjeYhD17YknvTK8NE1e5NCaLFYWVBhjM7P3pjXXLjSPfYgKB0OisvLQSW67xmwXO2T1HdJ34nHjRMTFSSm0NHA9lHQIArLq0CuVG6UazU7ZOwaaUTZi7ey5rOQVxxa7k0oYtE5cKDbXwb1GvbTObOnLp36R/vbZNuP0p0RsxcOAgzJs3DwBgFjFOaMir4jNbrDiXWYpT1/LRKa4zKIrCmTNnWHOuFVTiUk45skurkZRbwTKIpv58vE7vg6SEEwiERiWjIgMfHP8AAHBvi3sl5xZVFyFIFyQ6XlhdiGBdsOg4N5n1cPZh7MkQ70W0NGGpI1eHGaYC4FJXxh26hnbF6Xx+WflLPV+S3NektpMEl7uDklKKjlG3SF8dQuNQVGmAVqWEt4f4pT6zpBrVIt4YobmusPd9+nzJG/APDhWcU2lge1+Y1VIJaSWyjoUL8cwQCIRGpcJY4XpSLV+elq7sWZqwFAAc+TCukGoNAAA/X/hZdOz3S78LLq80ioefxHivr3C+jr0twJgWYwTH7f2J3uzzptv7tCPHYOEmGXcO6Vzn/RGc0DQNk8FyUx6VNSZklVbjWiH7+2rleF4Wz/8/nIg/hGXLloGiKHSOCsClC+ew6JknMahzK9zVKgJj+3fH8v8tR4netYFfUGHAob07EX9gLxa89k6Dfp5SEM8MgUBoVPQmveM5TdOSF9ezBWdhsppEc0jseSz5+nzBcXdw5TLPqswSXL7qsnwRz/ta3gfAJjinU+l4FVv2fJXBUYMR7hWO3Kpcx9jkdpMdeipRPlGy98lFKlxmZ2HPhXi80+MY+KetsumD/h/UeX8EJ2ajFT88t/+m7PuhJX0El6cV61mvX3zrfaRdT0a3Lp3x+htv4lpBJb77/EOkXE3C17+uhX9gEDJSryHYU9z3UWWwddAGgKKCfLz14jws/el3eOjYYnsGk8XhuWloiGeGQCA0Kt+d+87xnJkbsjllM29ucmkyntzxJHIqhSuZ7Bf3zdf46wLuKfNWmfgVGkzhPiEtmUF/DsI3Z76RvY8wzzDHc6ES7ZExIwHYvCdjW7Cripjl5lKCgatGixtXA5oOkDRmwj2d3agDPQKx68FdWHffOjT1aSq6DuH2pqLGxHrt4+sHtVoDWqlBOeWF4NAw5OfmoF2HOHTo3BVNopqhd/9BGDZqtOg202sNJJqmsXjB/+GhRx9Dh85defOuFVYJlnI3hKI28cwQCIRGJb083fGcedJ65ZBwYmpCXgKWnloqOKaklLBYLVh5aaXguJgAnBBvxfOrpXak7XCUSQt5ZopqxPVlXNHKvxWO5zqTG89PP88a52rlMF+brOwLEJNOIZ0wofUElkKxnftb3S8Z5nuk3SOs12FeYQjzChOZTXAXlUaBp5YJ6/g0JjRN40qRe+FQEyNvZeK0x/H8U9Nx+cJZ9BlwD+4ZMQbo0cvlul9++SWqqioxc+58l/tgklash8lQP4Vv4pkhEAg3DLmCbluv83vvADZjZe6euYJjAJBRLt39mVlSvT11O2988eHFso5PLhPbTnQ8X9JvCe5reR/mdZsn2Fuod2Rv1mvmZ9UjXLg8e0m/JQDg6BnFxUJb0CGog+DYJwM/kdUHiVB3KIqCWqu84Y/08mqIFCXJot89w7Dt6Dk8+sT/IT8vF09Nuh+fvuP6t7Fnzx6cSziBni3D0C0mGGP727rI9+jRA9OnS4swluhNqKgWN9pdQYwZAoHQqDDF7+qr1Lvt+jZH2bUQn5/6XHL9GdtnSI7XpYLpt1G/CS4/Ovkowr2cYZwwrzC81+89zOw0UzAHpndEb3QL7eZ47af1czz3UnsJ7kNM8M5O15CuaBvYFnO7sA3ACK8IDI8eLrku4faEpmlB4TspVGoNLBb2OoFBwbjvoUfw/hc/4IU3l2DdqhUut/PFF19gzb8H8ef2A/hz+wF8tWINAOCPP1bjvfdci1ZO+K7u3d5JmIlAINww7GEgV9ovdUUoD4ZJcU2x5Lg9IVfu8SkoBbqEdsGLPV/ERyc+Yo2JGSBSjGs1DqfyTwEAnuj0BGusiXcT0aRkMewhI6ZnJy4kDitHC4fpCLc/Qg4Zq5WGQiGeO9UkqhnOn05AVkY6PL28sOp/3yO2Uxe0bNMORqMBB3b/i+at2rjcd2BoBForfB2vPb28AQA+oU3QtGlTFGeWSq4vJsAnB+KZIRBuYQwWA6Zvm47p26a7vFDfinB7GF0ovAAA+Oyk/HYANxJ7pZVQcrIQdk9TgEdAg+x/ZMxIRHpF4r6W98FH48Ma+2rwV25t638j/ud4zkwCHt1cPJGTcPsjlEvL1XXhMu3puVAqlRg/uDcGdW4FtVqDLz58Gw8N74fHHxwDpUKJD78WlzEAgGqjWbRPU3lN43e8J54ZAuEWZkvKFsed+rN7nsXPI6RPKLcaw/9ihzIOZx9Gr4hegsmqtwL2i75U9ZDUevXFU+2JbRO2CbYYaOnfEve2uNehpMytfuLSM7yn43lDGVuEWx+hyqDUoirENfUXXSemRSv8tnEHa9lTzy10a79pxXp4qtkCjU2imuFshk0Erz5eFzkQzwyBcAtTY3F2qGVWwjA5nX8as3fNRmpZ6g06KnnoTXoYrew7td1puxt9v9mV2XVe11415Epsz449kbe5X/M675OLWK8kiqLwfv/38fHAjzGh9QS81Ve6dxUT5vENihpU30Mk3MKImQxcsbyGxmyRriW8mF3WqPsnnhkC4TZn2rZpAGz9iLjlvjcTocqg9Ip0gZkNy4h1IyTHrbRVsrlimaHMpRKxHXsib2xQLOJC4nCu4Jz8A60jI2NGOvRpxPhmCF8L59TUU6gyVsHfw7+Rjoxwo8mvqEFljRkxwV5QuFB6ttI0iirq16Ljpy8/xU9fCSfZd7urN7757S/Rdd01pQK9NGgb5oPDl/SuJ4MYMwTCf4rMiswbLniWUpoCK21F64DWrOU70nYIzr9acvVGHJYoCXkJiPGNER1/6eBLddrukGZDHMbMipGuKz8aE6HvgFqhJobMDcRotkCtVDRq/6vcMpvntlRvQqCXBoC0AF1OGVuBWqNSwGiWX2H40KOPY/i9DwiOaT08ZG9HDideHYqKGhP+t9cDzy91PZ+EmQiEWxhXypjnC9iemBvh+WBysegi7t94P8ZvGi87QflYzrFGPippDBYDvj/3vej44azDrNePtn8U+x92LUnPzJvpFtZNYmbjYO+A3T2se4OGvQh8UgursC4hUzR0U6I34nJuBTJkNGZsCIxmZ1m12ClDaLGHSrwJabC3FoGeGtYyv4AANGveQvARFhFZl0MXRamg4O+pwRP95XWNJ54ZAuE2ZvLWyazXddFxOZN/BlWmKvRt0tftdadvcwphjVk/Bvse3udynQ9PfOj2fhqS1w+/jgFNB8ie/2LPF2+L7tJfDv4SBdUFLG0bQuMw6JN9AACz1YqHezbjjeeX23KuSvVGNAv05I03BMy2BPkVBoT72dpliN3+VAlUNEndKoX7eUBBUSiWaC7pp1OjrB5Cdw0J8cwQCLcR/1z7R3Kca8wcyDyAGdtnILMiU3SdqdumYtauWXVq3shMUK6P1L8UXw7+Eve3ur/BtldQXYBSQ6ns+XINGbHmmDcKpUJJDJkbzPHrJS7n1JjcE7CTy/VCYU+oVcQ1k17Mzz2R+ma7ysHx1CjRNKBxDLW6QIwZAuEWhlsf8PLBlyXnc/VR5uyeg4S8BLxx5A2X+yqoLnD/ABuICa0nCC4fFj0Mg6IGQavUiq67+t7Vbu8vqTjJ7XVc8UDrB9DSryUe6/BYg2+bcGsiHgZ2Lr+SJ94bqzHIEDBaxNCq624CxAR7QSkhxCcHpYjB9OGETm5vixgzBMJ/CKF+Q4B4WTeLxq3clMRDJZw86KuxqYna80GE8FZ7u72/zEpxT1Vd8VJ74e/7/8aCHgsafNuEG4/ZYsX5zDJJfZSb+JMRxeBGQq9GqUAAJy8GAKJkhMZUivqbD55a4UyXJv7ue3yIMUMg3CFYrHx3N/POUirfpswgTyPCvo0dqcKVTGJMi50mOd4usJ3oWIguxK191ZX53dmdgKW8RYTbm4vZ5Wj16jaM/eoQPtx+WXTehtNZ+G5/Ct7YeIEV9rkVjRwuWpUSgV4aBHvzv8dCBo67tAnzQZCX9HbE/Dpdmvm7vT9izBAItzCuqpncWXfmjpm8OUwDxmQ18doPALbQVb/V/fDVaddy+p1/7YyNyRvx/P7nZR/nlPZTEOktXAkR6BEIQFxIbmTMSHiqPVkNGt3l3b7vio7FBsU6no+IYevXqBSkfuK/yuyVCY7nPxy4Jjn3g22XsSI+DbN+Oyl7+zUmC4qrjKK/b5qm3f7tuzu/TZg3KIpCY+S2t4/whYdaiQh/negctVKBCH9hj6y3iMdGCmLMEAi3MN+e/VZ0zJVeCzfpNyEvgTeHmZMzfft0DFozCNfK2Cfvd4/aLvbccmaxk+drh1+TPC4mS+9ZipfvEs8DmtnJZoBZaOEkyoltJwIAvh7yteR+pETy/LX+omOzO892PG/i3URw34Tbk8JKA55fcxYJafzmoxV16CVklghHcUu4r+RVILNEj3KRSqDrhVW4nFvhlmqvXXNGLvbEdgUFzHzoXnz05iLBeULhpBABb46dUX3ioFEpQVEUlAoFOkcF4Oev+UJ77cJ9oJUoDXcXYswQCLcARosRuVW5vOWVpkrRdcZvGi+5TTPNPyHvTd/L6juUUZHBm/Pv9X8dz8uN5dCbhRMKLxVfkty/HFyFiOydp8V6JfUIs3WD9tZI580EewSLjlEUBZ1K+A6Su3xyO1spvK/GF7PiZknuk3Br88bGRKw7lYkJ38bzxgz1rUDi2CAlIuXNepH9VBrMMFmsuJBdJlqdxKWgUl4LDi6uqvXahHkjJsjZAT7QS4NwP2mBvLfffhs5OTnIycnB7oTLeOSxp2Tvt124j+ByVxBjhkC4BXho80MY9tcwXCm50mDbzKnK4S17du+z+O3ib47X9qaFLGrPMVdLrqLvH+LaMw9vebjex8gMc7XybyU6L8wrDAFafrNEOWXTzXyaSfYxUlJKLOwh3FSPm0e0qNcinJt2DocfOQxP9a1TlkpwH7HSZgCoMoobM+54S2iahsVQg5qaaphqahwPi8H+MLCW88drUFRawRozVFfjWnYxa479wVyXpmm0CXNtGMx8/DGcPHoYK3/+Dp2jAtA5KgCnT5/GlClTEBISAh9vL3SPi8XGNSsBAGG+HqzfXasQ/o2Ej48PwsPDER4ejuDQMHh6erHGNUpx0+PB7nVTMCdBXwLhJqI36fHkjicdoZ0dqTvQJqBNg2x7Y/JGweU703bisY628mEhj4ddyfbPpD8b5DjkMqTZECSXJjteM7s+qxVq7HpoF7r/3t2tbS66axEmtp0omd+ioBSiGjFxIXG8ZbeDgB7BNe4U4+SV1yDM1+aNOJIiradktdIwWmxGsNVowJ5XnqjzMbrLHsbzwUt+gofadRhn6dJlOJd4Ca3axuL/nreFmn74YSkuXryIbdu2ITg4GMnJyaisqkK7cF+oOYaIUEXSBx98gHfeeQfNmjXDPWMewKNP/B9UKuc8qeOK8BPPs5GCeGYIhJvIuqvrcK7Q2ZyQeaE8W3BWcB2hqiQhtl7f6nLOgcwDvGV2Y4YSqDWoS2fuKe2niI4xc3a4mjr2smw7GqUGvSN6i25LKAnYU+3pMlFXatwe5iL892B+v3ck5qJUQum215LdSMq16cXsvyItLnmtUDw0fDNoH+4rOR7g7we1WgMPnQ7BoWEIDg1DVlYWunbtih49eiAmJgZDhw7F/ePGQaNybTI88tjTWL16Nfbu3Yunn34aP331GT5/j61z1TTAabB0jPRjjY3qWDfhx0b1zBw4cAAff/wxEhISkJOTgw0bNuD+++93jNM0jTfeeAM//vgjSktL0bdvX3z77bdo3drZsK64uBjPPPMMNm/eDIVCgQkTJmDZsmXw9nZfW4JAuNWoMYsn7T2/T7giaHnicjzRSfpuTyj/hovJamJ5QrgIJc2O/Xus2525n4p7CisvrRQckyoH1yj5ZZ0zO83E0ZyjAIBnuj7DGhvQdABO5Z9iLbsr/C6Xx6egGrcZIOHW5HyWU27gqd8S0DbMB//OF29zseF0Fl4e1Q4/HrwuOoemaVisNOw/HYVGi8FLfgIAdGzi75h3IavU8VyrViLURws/nUZwnLvu9cJKwdYEABDq64H8cts5xUNnMxjUAgaIn87piaQoClqOp2T27NmYMGECTp06heHDh+P+++/H3XffLfymOTw+6xnERtoMqLi4OORVmvHuovl47uXXodHaEodVDO+OQkGxPDWKOgrxNapnpqqqCp07d8bXXwtXGnz00Uf44osv8N133+HYsWPw8vLCiBEjUFPjPMFPmTIFiYmJ2LlzJ7Zs2YIDBw7gqaf4yUQEwu0I1xtxJv+M47lYBc8vib9IbrOouggmq3S/lIS8BHxw7APBMbsR01AXeHt5tRDM0mcu7QPb85apKOf919TYqayxGR1m8OaLlXwzUVJKyWonwp1BkgulXjnKutxcXYqioNR6QKn1gNrD+bAvU2o9YFaokV1ldYyptFrWuFLrAQOlcozXQMUbtz+MlNrxXCERR+P2i1IxDIjYCF+MGjUKaWlpmD9/PrKzszFkyBAsXCicV8alTRjb0dCpa3eYzWZkZ4o3wW2IM02j/oJHjRqFd999Fw88wG8ZTtM0li5ditdeew3jxo1DXFwcfv31V2RnZ+Pvv/8GAFy6dAnbt2/HTz/9hF69eqFfv3748ssvsXr1amRnZzfmoRMINwSuZ8LudRAas+NKwG7QmkE4nX9adPx84XnM2D4Da66sERy3GzFi3pSGRKyKCLB1q+bCDAkpKfbdpFKhxGeDPhPdnliCcZBHkGBIjUBgIqfHUn3E8qR0Yq4VyAtdMZtPMg0Ubg8l7o2KRqNBgE6FTk38HF6TkJAQTJ8+Hb///juWLl2KH374QXS/zOomFSenJjXpIhQKBQKDJCoXG+Dnd9NuR65fv47c3FwMHTrUsczPzw+9evVCfLytVC4+Ph7+/v7o0aOHY87QoUOhUChw7Ngx0W0bDAaUl5ezHgTCrYjUCay4hq9/IZdXD71a53XlIFYq7Q4+GnalBfOzeLjtw1Ar+Um5TGNGyHOkkDilhXmG8ZZNaT8FUb5RJMzUCKQVVeGHAynQG+v/XWlojl93/7elUrr+jkj9nu1jokJ5nL/1hdk3Sesi1yUmJgbHjh1DWloaCgsL8frrr2Pjxo1ITk5GYmIitmzZgvbt+Z5SLmcTjmPp0qU4e/Ysrl27hpUrV+KDNxdhzPiJ8PX3F12vIVoj3DRjJjfXFtMPC2OfYMLCwhxjubm5CA0NZY2rVCoEBgY65gjx/vvvw8/Pz/GIiopq4KMnEBoGK+T3UWEiJIB3o7BYLW6rjW4ctxGLey9mLZPqMi0mpCflmQGkQ2PN/ZqL7qcu/Z0I0gz77ACWbL2Mj7Y3fFPP+nIitQ7GjIwLrkXid1FeK8RXqhcJATdwDwTmT4H5XOh9LFy4EEqlErGxsQgJCYFGo8GiRYsQFxeHAQMGQKlUYvVq1w1dNRotVq9ejYEDB6JDhw547733MH/+fLz+wVLHnECBFgchPlp4qBRYfK942NkV/8lA8aJFi1BWVuZ4ZGTwhcEIhFsBqQRYKWZsn9GwB8LAVchlxcUVso0we4VRC/8WbinmilUYhXo6b26EjlMq96Vfk36iY/2b9Mfo5qNlHx/BNfby5GN18II0NmJquYnZZdh4Jktw7J/zOZJNJwFAarigwrbPGrNwuIrmPeFjtsg/XzBbAjB/K6G+fPXeNm3aID4+Hnq9HjRN47XXXsPFixeh1+tRVFSEv//+G82b828G7GhrQ0vtO3XG0aNHUVpaiurqaly8eBGLFi2Ct5cznMwNeQG21gbBPloMahvKG5PLTdOZCQ+3lV/l5eUhIiLCsTwvLw9dunRxzMnPZ5fBmc1mFBcXO9YXQqvVQqslTeAItz5iWi4WqwUahQZGq3i5aGPhKuTyecLnmNR2kqxtudOMUY4IXaBHIH4a/hM8VB6CxylliN0dKV6NoVQo8eGAD2WVs7tDxe7dUHh6wqtPnwbd7u3ErRjA++1omuDyMV8cklxv16U8yfFSvQlBIpXQJovNSimokFbqFbNl8strkFsuv2UB0wPC/KkwK5kaCl+dGuF+HvAU0Y/RKBUwutHNuy7cNM9M8+bNER4ejt27dzuWlZeX49ixY+hT+8Pv06cPSktLkZDgdKnv2bMHVqsVvXr1uuHHTCA0NELJvCaLCW8cecMtQ0Yq8dVd5CTD9lol7/cnZRhxq5Umt5uMXuG9sOgu4R4xjn1H9ELnkM6y9i91LEKf2dqxa93erhjmggJkzpmL9Mceb7Bt3o78l9KRErOl8y+lPDcmi1U6p6bWjLFYhS/67hgyAPv7zvwX1OffsWTJEnh7e/MePj4+mD7xAXh7NKChZDHxy8MkaFTPTGVlJZKTnToW169fx5kzZxAYGIhmzZph3rx5ePfdd9G6dWs0b94cixcvRmRkpEOLpn379hg5ciSefPJJfPfddzCZTJg7dy4mTZqEyEjXJZcEgp11V9bhzfg38UqvV/BIu0du6L6XnVqGXWm78Pvo3+GndQpEXS6+LDi/2+/ud4AeHDW4zsfHRawkvC5IGUbv9mN3q/ZUe+KnET812L6FvEd+Wj+UGcrwxT1f4J5m9/DG2wW2w8CmA7E/c3+9928uLnE8p2maJBn/B/hiN7u56xP9muOnQ+K6M1zyysW9MmYLDZUCyHfhuakTDfTVmzVrFiZOnAiYjUBR7WcR1gEAoNOJVya6nQ5UVQR80QVoNRQYsUzWKo3qmTl58iS6du2Krl27AgAWLFiArl274vXXXwcAvPjii3jmmWfw1FNPoWfPnqisrMT27dvh4eEs81q5ciXatWuHIUOGYPTo0ejXr59kiRjhzsNKWzF712y8E/+O6Jw3498EACw5tuQGHZWTn87/hNTyVF6p8zdnvmmwfUjlikTl05i81wLPGnmnlGWn5J08ZME5iTIVfIN14s0f60qMX4zj+Su9XuGN//PAP/ht1G8YFDVIdBt1zWPiwXzvbiZM/5dQ/IeNODmKuEzyK8S9K1fyKlBUaUC1RF8ouQRxkmwp0RciWC2AsYr3vQ0MDESrVq3Qqnkz56NVK7Rq1QpNmjQR2RgQ6aeDgqIcLSFccvYPwFAOJK6XNx+N7JkZNGiQpFuNoii8/fbbePvtt0XnBAYGYtWqVY1xeIT/CJeKLuFQli3WvbjPYhezbyzMMFKFkS3KtTdjr+ztnJ12Fp1/FQ+tSN31f/qz7eToV2XFt/e67tUCAIlFibKPTYpp7aexXr/T9x28dOAlWd4x2mJB5f4D8OgQC3UYv6xaiGjfaHw/7HubdozAZ+Kn9UOX0C7S+22gshLW/q1W95oB/Ye4WbbM5zuvwE+nxuP9xBNXXZGyZDRaviKeR+Xt0bCX0KzSarfmtw7zwVUBsT8lT0WX8VrO17sgCbAYAL+mgC4QUHDOGxb3cvl0GiU6RPrK905y9ydnFbfXIBBuMVzdSQv1H2KiN+mRXi6uTplZkYn5e+eL9kqS4r1j7zme/37pd7fXt6OgFDjwMP99tE+nsXp5ACoPSicuAkCLXPkX6Ulb5CX4AtKJtQEe7E7X4V7hWDFqBUY2HwkAKP71N5SsFk6CLlm5Cpn/939IHjgIlYcOu3U8bQPbyp7PpaGMGfZG/5uemZIqI345fB0bTmeKdpO+GbZMRrEey3ZfxdtbLrotI8CEbxSwGdpenpHdWOhkNJIEALc7BFhqQ11lmUDuOZunxg5NA6XCCdRSuBVmddFPTXAVt9cgEG4xmCEWoSaMc3bPEV335/M/o9eqXhizYQw6regEg4Ufr164fyF2pe/Co1v5irSuuFR0ye11WNA07j9iReXBQ1AK3K28tdICRW4BMp580uWmGuui8v2w79E9zL1u1gBgLixE3pIlyH3zTViN/Du9ip07Hc+zX3hBdDvWqiq39y1FqK7u5aEsGCfv+lxQb2XGfX0Yb26+iPl/nsWms8Kq7NkiZdBysFppmNwoR7ajZ4RqXJVT14c2YT6uJzUyoT780E2gF7uKUKVUINhbi2BvLU+hVxbMHnI1pe6v7y51aC9CjBnCbQ/T4jfTZpZB88+1fyTXXXpqKev15pTNvDkZFU6dIlH1TpqWbBppJ6cyx+UcJt2SaUzeb0XGk0+C0ktvX2Wu+0lbo+ALWTGZ3G5ynbbLzGHhYq12utRz33qLP4HxWdMmYaGxij17kNS9Bwq++KJOxyfE/O7zMSx6GL4eItxTrk78B42ZrNJqpDP6FYkJ0bkqRZbigW+PoPeS3bJaCTBhOgHGfnW4UVSI1TIUgW8EzFYCdoRyeSL9dYj0F0/SdeBQKgb0ZjUsNAXWrZDADWODkncR2M4QzfyV3w5JCGLMEG57mI0XH9v+GEasGwFjbUz35YPCSrJifHTiI94yprF0JPsIa4ymaczbOw9xv8ah58qe2JexjzXODYFdKnbPUxPCqAQtnP8ia+yuJPa2V31sweS9Eicaievpe/3eEx8E6qx3I9V7qSbxouN52br10J9m95NihnuslZXIeeNNWCrZPWpy37QZQYXffFun4xMiwCMAnw36DAOaindQlgU3Z+Y/hNliRd8P9rCWNYa5djajFEVVRlxgdLiWA9PMuJRTjnUJmY7XDaV3oqBpFGdn4p9nhMUYFRSFCD8ZxsPNwmoF9EWAhWPoldlC7pVmDcpNWhTWeEonPjW0of5dX3ZOTtYJWasRY4ZwW9NpRSdsu77N8fp84Xnk6fPqlN8CANVmfgIes99PbhW7jcbV0qvYne7USlp8mJ2AzDVmlp5a6la1jJVxDqmJPwaK4TJfuJ6/nfuPSiTcS+zHnsMiRhNv8UoFoG5hlKqj8azXaY+wvT+WklLW69I//+R7YG6XShkRY6bq+HGeEXc7UFNrEHiZq6Cz2LwzUt8BbsfpawWVmPa/41i49ixSZDRRdDdUxP1a2AXrACCjxHX3ayZtRUJJAwr2Yfn8Wag5s09wvHWoN6vZY11oGSLeZkNdl3BRLTRNw1pwGShNByo5rYH0Ng+b0WoLa9O8Mwfnf1HjnqHp+uDqZmwSY4Zwx3Kt9JrLOYXVhSgxOPVCuCXQBjPbhc7VVeEmk14vu47XD78u+xgfPMT+Ybd2s1n823eLVwraCdFJdLOtZWrsVPd2LAcBQ8TCaAprTEnhjZvSb8/WJELXeUtZGdKnTUfaI5NBWxrZdV9HKouLUFkiHD7SWAx4PONXPJG+AnFl5yUNjv4f7cVFhuDcU78l4MCVAvyVkIkhn7rW9BHredTpjX8R8/I/yHchKPf2louOVgAfbBPWdxJjwxzhBPd25TYva/w64WpbrVrp0lvVPNhLctxLK54IyzSUvCXmCVGanYH8SjWqLSqgqgCDBg3EvHnzxFeQCqFb+CHgf/75B7169YJOp0NAQIBDO64xIcYM4Y7klwu/YNzGcS7nzdo5i/WaadgAfOOGm7Gfp+fLn29M2YgCfYGs4wzg5LYq3HSAtPVylqUGuL4BFsVVW4LXer8GLzX7xHxvi3vd3s+Vu3rBWiNx4uQaQLeAZ6YgPRXpF87xB5jHJnC3aS5mGAl1NGYqigtRlCleiVcfzEYjvp89Hd/PmgaLmZ9zEmAudTwfWHwIa046QzllAs0UR39xEIDNK5Ccz/4y/nNOOpdMyLH144FrqDDYjmvsV+xqPqGc4exS2/dq50XplgRMKouLUHwtCZ88JCGLAArT+0QLjkl1q/bWquDTQIq5LRgeHDlaLoYa201YmdHDZtBwbsosNAWTlVFwUJIqvrHyTNbLdevWYerUqXhsykScPX4Ihw8fxuTJAjl3NA2YahosTEWMGcJ/gogiGm//akbna65dlI9tfwyfJnwqOv7tGWf+RVIJu+Pv5wmfw2xlnNg511JuEjBrLoPBa9mKvU+3niFxxE4UtXe/flUyTwA/O8uevWuE13PVviDKR7zrvL2KqXVAaxx55AhOPXoKp6aewt6Je7GkX90ECk2ZmeKDHOOFFrjI3mh+fWEu1r7zCsryuRdJ27GeaxqCpU88gsIMTjkrTaNGpUSRl0edq51+mD0Dvzz/f6gsLqrT+lLUVDr1S359YS6snMRPC8TLgqcvPw4AaF2ZjDAD+3MRqm76dAe/szbT28L1zNA0jfe2OvPPuMq6QhVQRjerooK8NPh+9nT8+cZLCC2+whpTMn7Xhmo9Xh0j3O3ZU6NEpJcGMFkFHhZYjRZorLTIuBVWo0V0zL6+/RHj64FgrQrBWhWsRvmd7WfMewP7Dx/DsmXLQFEUqCbdcORMCv5vwfPo0LMXmnfohNZ9x2H58uW2FaQaYZrNeO655/DxB+9j1oOD0SZIgdjYWJtqMJeiZKDgkiOsVV9uWqNJAqEhWbDBgugC4NU/rZi4SAEKlKi+zMm8k6zXajMNk8p5kfzm7DeY3WW26L70Zj18NbZucgrO/YDe7F48HgBGnLRiyPs/4dK9ChzoJH1/8dJfVkx/XoG4a+JnlBkdZjiSoqlVG1ljrbJpJLTmejekj2/FyBWiY3EhcY7nCkoBRW0c367uW7blHxR9/x1okxnBs2fBb5zTG2YuEPZOmQuLoG3VSniHDNc6bTTCUlgoffA3kNK8HPiFOnVH9MePo0qjQmZt58HfF83DvN83OFewWLCnQwwAIOLoYbQcWPeWFIWZ6fAODKrz+kJQDJG/4uxM5KUkI6K1Tb+HpmlYJbxiZzJKEVtxCUMK9wEAvmzu/D0J6dEIhZHuWuLMReOuczFHukeSWWAfQz/bj+OvDGEte7J/c/x4ULgdwX1dIoELtuc1qZcAxDjGlIyWH7TVKqoETJusqPkwAYEix5kNQDwrxjYutq59nIvdxxX59t2gNAIGJ8cofWfxa8hIS0HHbr1sAra55/HSx8tx9WoyVv7vJwQFBKAsJwnVXrWK3QLyFXZOnTqFrKwsKCgaXYc/gtyCInTp1gMff/wxOnbs6Jxo0gPGWu9cWTrA6dNWF4hnhnDbo7TQiBa4Ln520nXzxZ5JVqz82IIxx9l3bZIN4RhjDSF9P3OnbRtzt7jels5oM75kh5tk3J2Fe4p3oAeAEE9bTs3QZkN5Y2Ge0qJh2QsXwnA1GcbUVGS/5Kwss9bUoHLXbsF1CpbZ2inQOh2KPT3A+lQYFynD9VTJfd9ouCHGqmNHcSrG+dlaOOXlNOO9/P3NZyjL5yRiukMjlH5THMVi5vdeb7Sgb/FRyfXthkztyo6nVoFjrahhe9hyytiJ+Nx8HFcJwZU1wh67pbuvsoTmejUXNwD7lDsTsxWczyKqhu09TNwv/F223opVbBzvsa+PDzQaNTw9PREeHo7w0GDk5uahY4dYdOnUCVFNm2LogF4YO3asbQWhc15BEkDTuHbNlof45rtL8NpzT2DLiqUICAjAoEGDUMwMqxbwPXEAgJK6h0yJZ4Zw2/PHR8L5BsyGiXHXrBh1ksaPIxUo9nVedJ7dZPthTt9txT93OU9YVtoKvUnYy8I0YIRUfa20FQpKAStthdJCY/ouK843p3CijXv3DhqT8Alb4eL8yPSWyOGDAR/Imvf5PZ+j04pOrGUT2wi4j2uhJU7kZgmPiqXCdtedGOiF9GA/RBWVoVOmbb7+qPMCqvCw5fGYFRRUjSiMVldomkaFTiLXiHNRWL5gNstzY7VasPvnb9GkXQfE9uc3xbzxOD/jRV+tQ4dq9oXHw8K4SHIMlkFFB7AveCB/iKYBikJxlRE1Jgs8ag2N3ZfyWetzPTcqF60hnlstXCFmtlhZSfliCr9+plKc37Ta8dpqZhuiw4rYScvbv/kcz85bBtDsyp6inHQoZ4SiRBMAM8W+3HZsYms6eyW3HEYL//trl/8XK0vXqRVoGSou2kepaz+jijxbn6PAlraWGqUZgFCIkPEZT5vyCJ6Y8wzOJyZiYL9+mDKmL+4e17V2wwrH9GKjDirKCj/oAX2hw3h7dcEcTBhj84It/98ENI2Kwtq1a/H000+LHi8AIEPaQJaCeGYItwUF+gKHdgxgu1D8deUv0fmbr22GyWqC0kJDY6Lx2p9WdE+h8dxGtuEj5iinQQuqAQPsPBihUNbpfNuJ9IdzP2DQORojT9F4YZ28O7Qmhc4TyhsrhY200DLpROCB/j2xpN8SbLn3b1n7lMqJEWNQ1CCcnXYWaqVEAqNAUqv+pC3EV3PhgmNZnq8nsgLYznZ9QgLSg20n+4wgP5hqL15WvR45r79hm0RRyPP1xI5OLXA1jN024VbA6qKW5cTeHazXXM9N0pGDOLdrO7Z9JZ7f5eAGJEIz7QnjdX7vrmEFNu/Ehg3b8FTa/1hjnSqcmkJzVp0CAIQYCvB02s/oXGZLnp7+v+OOOdywDTfMJCVYZ7HSKKoS1kVaczITNSbbb7FJdRbSN69A/8pTuKuErWXSpIadkHz58AHHB6C11EAtcG4Y17UJAr3UoCgK0UG2ZHjaagWlVkCjNANqBeuh0Cih0ChBq5W8MagVUGpVUGiEx6BWIMBf59iG0MPhKazItoV0qmvzqgTkJwA4wj4WmsKQgQNxYv8+PPXYDOTm5WPIw7OwcOFC27zaUmyTVQmTVYlqS+05wGxAREQEACA22lkhqVVY0KJFC6Sny/C61EH51w4xZgi3PJeKLmHw2sHo/nt3dFrRCc/sfgaT/5mMt+IFVGNrWX91PbIqs/DtVxb8/onzotqek1sq5uWgaZqXW2OHacyYrPyqDbvR9fWZrxFY4Z7H4PMfncfaWqTAo2MqLemdqdq6DWNbjoXnSr76ce/L7OMJqKCRMfv/UHnosGAYSYwuIV0kO3UDECw3zlvyPgAga9582xwACc0jcLZZGKrVzjtXQzK7LHt3h2iUe9hUikvXrLEtpChcaGo7aV4Nl8osuFGwL7Bpen4DQCbH9+2QHK8ub2D9jvrCsGYoAUMtpjod+vIyXFv9NbS0uMhiYm2J9pDCfdDQJgwotvXdOnZdPBGU63iTkuQ/cFVepeD43E0oPLkfXQqOoVfpSfiYnHk49xSyPS8mQw3G5tkaTjbXp4pu01OjQutQb/jp2EY+1/Ryp5S6ZYg3S+W3ib8OLUK8eZ2xmRir9ci/noL81GswWWs/K5oGzAbeZwkASpUGFkMVQFuhN9uOPTgoEBPHj8fXn32Cj994AT/88INtssWIcpMWxUa+IGD37t2h1WqRlOJMdjflJCI1NRXR0cIVXyzq0GDSsWqd1yQQbgCJRYmYuIUdytiXuQ8Xii6IrMGApuHvIh9XxTAKFIxfuRVWvLBfuB8Q05gREtmrNlfLqiTw1tN4equw96XHFXFrJayUZh03j9o7MiFV3IEX2Mf1xL9WVO7di4wnnhDsON7CrwXr9d/j/saLPV/EtFhbN2xTfj7Sn3oK+oQE3rpClUY1F9mN/y5FOnMWTPYLFA1QnIuVVaHAmWh2fk7hV1+xLqnZh/hessrDh5GzeDGsevcTs92F6xxJ0TecMfLvqy9Ihu0aBc53OKPYqRPATIBlUlMpXv+vYKwTashHiNEZavQyO9czWax48S92qfu7/7C/N0K/rzUnbBpEpjoq/Kpp542JQsRYA8S9uSajzVtj94joJYxRnVBirgheWhVCfTzQItgbYb4eCPTSwFurkmzcWJydBavVCqvFgiKDp3OguhQVJn7oMyIyCsdOX0Dq5XNIz6/ER0uXYfvOXbiemoakK1exdfcBtG/vTNK1GzxsKPj6+mLW9IfxxiffYcf+eCQlp2L2ItsNzEMPPeQ4Bh7G2t+nuW5K4wAxZgi3EFbailJOE7MPj39Y5+2563j/aZkFfpW2k1hiIduNHlJKI6rANsZtacDlrfi3WPk6YkzbY8WQs/yTpq6GxoucsBRz1uiTNB5vP93l9uXQ86pzy4EebO9Gr4he+Pyez1nLWvq3xNTYqY7wUsbMJ1B14CDSpgg04RQpmzbnOF1OqSH+vHFjSgpyXuMbVpUeGpTW5qBYjUaUbdwE5n/534/5LRkyZj6B0rV/IX/pUsFjsRqNqNy/v87NKsvOiitNF5vEqz5Mue4l+15IvoQTSz9xa536wjUY5q9xvtcwQz53OgAgvVj8c+xdcgLVtQ0gH8pezxrrX+Tsii6kOZNTVoMtjOXVAr2aXlxnM4CYyrhe5iqoBLynQsg5X/gqTJjQXPiyaTWxv+/lBcKfEQCEMZpDCin5Ngngez28PVQI8/Vwr/s0CxoAjRoL3ys0+4mZUCoUiO3eBx3v6g21Wo0ln3yKwfeOxQOTp4Cm1Fj9zQeAiNQE86vy8StzMGncCEx9djF6jpmKtMwc7Nm+GQEBtaHgEoHqsdJU29+DH9fxvRFjhnALsXD/QvT/s78j5wQA8vXiJwRXUG7mhHrXAFP31CYEb2cbC19/a8GnP1ngV0Xj3WPvSm6nuKZYVF+GSWSR8AEuX8o+UVerVdjVIQaXGaEU9Z/OFg7nmobgTBS707Mcz5ArdeCfhv/E88xwMVy96nie+vAk0Izu12KqtrRIsm5moOsOxDn+ttwa2mAzFIyMyhQpzRljcrLg8vyPP0HG07OQWRv2cpcjr77kfOHGRab4nLARlJ/qVKW+fvYUa+zgMWGpgcaiRM++S2aGloQ8FwCw+50FotvrXnYax2ubUXLX97I4PWf5FcKiiX8cd+ZcjPnikOAcwKkn42WuxOMZv+Lx9F9F5zKhZPxmPoi8hrITwpVLlButCxSMuUItD/x1MsT0qgptDy4Cirx2jEazQHsCoGXz5ojfvAL65CPISb6C+XP+Dwf+3YbrF87h4snjWP7dt2ge4Q9UuGiUW5ELtVqNT16fj7yzu1CedBA7V3+LDmG170eqSWUl+1x/rLApdue2lF2oR4wZwk0npzIHX5z6AjvTdgKwqfPayarMqvN23TVmAGBAovRK4bUCwAl5CUgqFikvhLhYHhOxJF7u8uQwf5hUSlxjJLmaa+/szQoKmUG+yA70QWaAj+MSYUgSPzY7crxH7lB99iyKVzql3Wmz2PZtR2ng3JHm+kkpbrAp/OZbh5eGSeXBg6zXBpUSJZ5a0cTC0j9tooJVnPXsWK1WbH5zEeJ/X84bM5eWIp2RvORKfBBwGivFpcL5IVeP2/pVmQw1SD3DD92JUVnU8Ho7L6w5w3qtqW02ejWvAlQdJQleWCtsxEUabN/nUr2R1UeJyZEU18KAV/Iq8NkOm8Bd0xqbCotU/g4b1yeMSwf3uJxD0zRLcFDuttlIf5doqwWW0kygLIPfKFLIwKmluFQk+Rf8vCRBqgphtoqYDeZq18aOlJIwp2T8UEFznCmJRCEzTCYBMWYIN50ndz6JH8//6HhthfsnyhqVEmlBvjArKOhqbL/KmFrhUYNKiaMtI5HlL/9i6YCm8dzfjIty7Q9+xvYZeHDzg6Kr2RtSSp2SWrn43dthGmU1KvFY+7lmocgI9IUpJxcZTz7FGrNQFMycO0BuuWnRL7/g5Z4vQQzD1aso+vlnWI3iFwfjNadnwZGoy6X2VsvMMWZqNCqb4SGB3TtQvHw5rkTwk36573t3hxjEt26KjNMJwm0SXJT4Xlj0Eq5cOo8jm9fxxk4snI9KnTMJMz/RmedRnC2sYnzlqM2jIJb/Yl+uL3Mv3+bf75a5NV8O5zJLWa/H5tm8geM+3o4gU4nAGq7JrzBIGkJd3t4Jg0AISS4VNSYk5dkMCalr86i87bxl9a0Hu37G5kmrqapEaR47jCiVrCuEKydPSU42Cmq8YLQq+LovAutaaAool27sZqVdmwNLvvgZ/m37oGVcF9bDp01fjHrIRejbUGErEXcTk5jxxIEYM4SbTlo5V+bd/W0caxmJxKYhSGwS4khyfX+F7aSYFB6IYm8dztYmkHZNlm8stcoG+l6SPiBvPY1nN1oQx2il8MCmB2Tvo1qtQqG3Dln+3oJv3V6iDABHW0WyxujaR15QOCwKBbICvVH0/fcsdV0awM6OzbGjUwtYGCc6v33su+T8Dz5E6GG2bDtg87jUXLyIa2PvQ/7Hn6Doxx95c+yUrl0LurbEuPDrrwXnODVm+GfdE80jRLcNAHqN0/1e6CPvjg0Airx1KPjyS/6AC2OmeI/4nXhuCvuz2vuX0yu1bolwM9FjG9aApmns3CwsK0DXuuEvH3bdfLExqSotwbRMfgNFmqYxM0NcEVoOj2aulhz/57zNyqdoK3xN7l38mB0LhMIpdlrp+XkbdkPZQ123y+KRNTbNKZOA0Sx0A0DTtGg42FVejLF2H8UGT1RVuP6MKk3uGVNizJr6II5vX4NdmzayHqf+/QM/feyigW6RcKjXFVL/RybEmCHgkxOfoNOKTng73nWH5RuB3TPDDNUEVNB4cpsFUfnCP/6q2rLdPD9P+OrZc0wczYqHDtq2L6e/kRyl3Sn7rOh30aZlww3w+jCKaGLTBJoNKijsjY3G8ZaROBsdhjxf9gW6QsuOneu17JMSTVFI6NQHvz40F1uGTBT84VspwFp7q1ejViEu2Caq1w585V+/g+dZr7NfegmpD0/C9fETHMuqzwk0VWRQ8gf7YlXorUM1I7cl7RFb0zl7WTXrWF3ckubWxbsG24WqKj6ev1ziomG1WpDQQti4qrl8GQqBC1HmxQswGQ2SyZ/XEo7DbBbOa7C40XDy2qkTridJUJSZji+mP4SdP3zFGzu6/k+oBMKQxzeKazvJxd8s7HWyVzulFNiSiMfmbcX0zJVoUeW6u72dnw8x57rna7HnzLx3fycXM11sR2C3xmrniUCrUoKmaRSmpyLvWjKoeoZ7K4pLYGV9b/gHUG1Ru/RwGKyuK6wCA/wQ3rQVmsdEsx6tmkejSUSoy/XlUmxgJEAPEK4q5UKMmTsEk9WEE7kneEJwhdWFWHHRdqe19spamASSx8oMZViTtAZlhhuje2FX2H39sM3Sp6w0vv/KgmFnaHz8M/uHH1ZC80ITDx7mXGQ4L1vlAmoTjR+/cH0Sscj4hQQzPpYPfmFvc8Rp587fXOU0ZjxrQ2EGTtionJMHYpIIK6UG+WJXx+Y40bkfAOBKy4687QHA5chgx3MKwPK+X+Ho5KOw7jmMcg8NjrRqgkJv28lDwclzsVUMcXARXDdmZCD1kcmgYfO0HG8Zib2xMax/Aw2gyIdfsdFYWBQKGK+y7wxpmpasYqouF7/jrT5/XnD5n2+9jM2fve94baX4iizpK38T3a61Nv+hQkYOzIYPxXWW5PDL8/8HU001zu3ejoyL7PfDvPgyOfRH/bwy/qZS0bHBHG2X6GpbqfWY/H8dpdsWK43zmeLnoX8TnU0taYZVESpSfcWEAo0BbUJwXye+kS2XypJi1LiojAvx0aAwPdXRiVxXXf9GiyYDwxtkFP7fFbnIPakwaVFqFO+4bbAoQdOAUcDosdJsA6rSrEaRQQezlRJM4KVp5/aEWH6th3PbTe6SPG47xJi5Q1iasBSP//s4Xjv0Gmu5wWJAaAmNcfFW6Aw0imqKWEq7gK3K6J2j72DMhjEorG78xn521+vma5sBAH0vMqsowPJ+eFcD8a2bur2PscflxbLMnN+t0L0ec1kLZqhc4Jc65IzNoNGKFBxwt6+QMBwu1no2mOtUa/lVEGmMMBUApPS+G6r0XNRcuICTzcNR6uWB4y1t4SuVVcbdbG1eh7lEOG+iZPVqVJ8+jfQgXxQwPE0XmziNqjKGAVrp6Q2jyuZxsioUOC/gsakLTBd+SliAI/xlp+rQYe4q7PETbM8HK8/FbBZttHj9tE1s0Uop8OnT7+CTWe+iRuO8SNhVkIUwFds+07M7t0oeWyqn0qm+rHlrEa4zEo5LKoQviEp1/cIVUzP/EB1rX5nE0pth0qVWJXj3pTyM/Uq8kokJ85dzf85mxoDwb+rLSV2wfEZPXImXt30hrBIVdWGeSnhpVfDXaRyGDABYTSZ0jPRDiLd0vpgUTIFFS437eSl2hMq27ZQYdaIZAFwBvUqTFiarEoUGLxQavHjzK8xalBh1qDC7fs+XD8ur4iPGzB3Crxdt5YnbU7ezSp+ttBUfLrdgyj4rHttpxbC/hmHUulGsdY/m2PpllBnKcM+axu8Rw23e2LS2hNn+Q2Kq2HZIF/55LVgv7XWZcFhe3oxBRoVkE5ES6/YZ/GVPb7PtV9Rm4GxKSGm12It998QtKa1kGDR2jwuX/I9seg5GjifHK68CI2NG4oUeEq7d2v2VrlkrPF5rNCRyjJK0YD/Hu7EL5FXpvPDttJfx1WOvOOZl1HaZdpcSbx07ydkkrS+S8eSTtmPQqHifKQBs/4Kt62JllJVyDSMungYTiv2dxtuhnkMkZju5cEz6Qnpiky0RWSwnpz6sf/8Nx/P4y8LJotXmOiS0uYHGrgnD+U7bvSxP/Sa/wouJljaidaXNMzch52/BOUkbf4NSQWH7N58LjgvhrRLXEuKiqCxCyxBvVlm2Y0xBIdhHC7VSgRAfLVBViMqcayjLz5Mls8CcUVDDNx4aCoNV2NjhemaYWATG7KJ7wuJ7bC4f3ivr2IgxcwfA/TFM2zaNMQh41f4eO6TZ5uVXu6ftQtM03jjyBubtnYftqfwqAQBILknGl6e/RIVRWuId4FczGVUUzkSFYk9sNEwKBUYm2Ma7Jlvx6F5ho6R3kvAJwF4OrJYZppZT3h0k8pa4uTuO+eU0w53C/qFzjReDynnyKPfyQ4WXD462asLZIncdp4Fi97hwp1but7n0ue/PnJODD+96G9M6TIMYtN3YrIN4VxknjJYdZusLZVGqcLw2XMY4TNuc0Ka41qyNrO2fZejtlO/cyRrTa4RPxPvbR+Noqya48h5bP6jAk+2FyLns7C9UczmJ916Y+FfVQMEwfk536oOCQLuCMf9zM2ic20pZz6+csnNgJb9EvDGI4TSRtMNV/tWaXEsQuEdtJWI1uygg2Chclj20vXjXdm7+WNeyMwCcZeBc8q4lY//v/xMcE6Odr/xzpT2vRcw4USsVaBfugwgfDVCWgUq9BdUV5TAbZRhMZoPkthuKMokwVENRLeEdkoIYM/9xKowVuO/v+3jL7V/6upRBc7lScgXrr67H7vTdeGH/CziRy09MfGDTA/jh3A/45KRrFVPuD7JjGo3sQB8Y1Crk+HsjttbjsWitFVUatmVvVvLjuXqGp2J3x+b8/QFIiA7D2aiGCW/YETOE+iXSmBwroJgLvqfkZG0CqkmpwvdTX8B3U1+ClaOZUu4j3GTRImBsuEqwBYDcd2wXdWNamvCE2tBXwWefudwWlyNtmoIGkF2byMv8jPb3GYkyH38AQFqw0zuzcvwsrBs9DWXe/oLbLPENxI+PzMPZ9j2Q5+8Na3U1LJVVyHx+IWtepVYDCycPhunJydi8UbJlwIFVvziel61fjyKJaiqaAhScbf0y8RnBucc798MXjy/G+bbdAACb1tQtLyXzMr/5IwCc270d+377uUEudErO+SKoQly3pC7Y/xvBBrbxYs+fYaKz6NHpyFd45vq36FjOf+9cY0bIy8nl5Ob1LucwuTtERvNE+/HUfh+kksMpiuKVWouJTLIwCwsNukIn1dUdgEorP/RllhOilsHxQvcb3wLEmPnP8+P5H2G9kocZW6Mx9EQoVGbbF+6vq3+hsLoQFtoCs4LCgTZNkRngfrO+nMocfHmaXfJ6pYRf3mvnQiG7p5K9t5HaRENlpkFZaVaYKaSURsc08R/zoTZNUe7th919R6PEl3/8+T6eqNBpkR3aFOtGTUURw/UP2HRbcvy9kOfvjaxAX1gBvLzG4shVYcr9A4DGxH5tb38gxMSDwhfGKfuseKL5IwD4VeipIf48PRgAqNY5XcdmpfPOhenN4JIRxFfUZfZCAgALQ+vFfrRl69fDWl2NlBEjhTdstTpKTS9HBOIiZ5sAWFVZJb6BsDAayGUF+CAr0GasVHuwDQKj2nbyvNjEZlgyP5+dA8bajk+ngV6tQrFfMH5/4Gn8NHkBSv2CsWPg/QCApK7dcKVHD1xqwv5fn20WitK17GqclFCOIShRTZSTchXmItfCbQCQE+DDM2bsXI4MglmhRHJ0W+zqOwb7+9g+5+33jAfAD7PK5c83nBpBNVWVuHRoH7afTMbOH75CwpYNyBIxdrgUZ8sXqpSILtSR2pCyDK/ftIxVMJfacvjuKXKdV6Go4+cqhVqq46sI1QKl1FWl7Pwzpt1ZnJ0JK+O7ZBYIcRrq6M3Q+kp3m1e4kC5gUmyUL5XAhGtjCyUYy4EYM/9xll9YjsGnbK73pgU6dEqxJYO+Hf82pm+bjlWXViEj0BeVOi1y/EW+2DQQVKqBQuA8/9i/j2F/JrsKgauEWlIjLrC1+PBiKC00VnxmwaqPLfjtUwuoxKvQm2wJiIEuolIWpQLrRk3FqU53Y83YxwDAUQZcoVXjZIsIZIY3w8rxs3Atui3Wj2J7RPZ0iMGZaGeJsl6rRrcUGl1TbL8wbhjrtT+tGHPctqxdBo0fvxS/+DWVuO4ZU1MBAOm1Hoi84Aic7nAXaADVtd6mAm8dDvUYjI9nvYsKL6engmZUyNgvhEKYBDrQFviKx9OZF5CS1X+KHzxN2wwaANdCA5Aa4o8UTo+lhFq9mOtNW+GnyQvw59jHHWMVHs7wjf0CLsbmoQ87nl9v1hYmpQqH20RhX2w0tgx9CDlhwndxNPiJzyaVkle9VK7jlLrXnlnFcmKuzJoFq0F+noQYB3oPx4ZRU3G6U596b0uITZ8uwdYvP0Hix/Mcy1xV2QBAbvIVZDDE/1yhskhfzJ/5RUQ40QVWF2XVGqsBGlo8b4mirbg3nx3yDjYVI9jgXgFDRz/XPbQUDeDd5lavcRNqizKcXtLSXBG1TaP7/cW0XjYP6fjJj2Lxu+zeZsFR0W6Fkq00hX1HToJq0g0RrdqwHlSTbjhxRtiY5ubhCDXClAMxZu4wfKucX5z0inT8mfQn6yI2/rBNK6XKZPthHMo6hE7XfDH2SAQGnrHdLdsNDUBeu4HfL/3ueJ5T5fwhZlVmYce17fjkJ4ujC7TGDDyyvgi9VvUCYCuNvsC5w+ZSGGQzRuzhlr2xMQCc2jN/3O9UhS31C5Z0NpfUJoJqzICPSM7L9N22gx17TPgkFlbs2i2c8fQsAM5Gi78+OAe7+t+HpJYdHcd3omUk4nsMBgCseuBp58oUcE2gQSMXg9q9O5xKD2c4rmQVWzCN+Y70J0+ics8e1vcmKTII1Wr+3eG59rYSy6yIaMey1BA/3jwxklqxNT9oRoiN69WRQ+E337BeM427Uk+twzNDi6gclyVfxYW+fV1+J23HKn4hONe+p5zDrTNCBom9fNdsNOLUNoGSewArX12Ay0fk94CSeo9n2/dAs6NXsGXiHBzu7l7hAC3QfqJTudOra69uYq/k/JY+mLNBcLuPZIskrYtwT3iKyzl17vsogYnjnbCYzQ5DWzSHpiRVtFJLDCmdJYVSCbXGPcOiT/fOyD61A2fjDzsekyc+hJioJujRORYAX9HXTLPLt69XuR8hAIgx85/AYrXI7mHUPJd9Zx5QrsZlRphg0gErel6hkV1pq2aYvWs2uifZjIToPNvFQyqMJHZ8dqpNzhj77rTdiE2n0URCZsGiYCvgusPVMGFP08HOPQSXA06vAUUDTeRFFHi896tFsqRaisLaJFGhUJMdGhSSBEI7jnGKQq6fF9KD/WClFNjXewSuRbV2ue/DbZxeDoWH8yRmpYDDbZqymllmLXiet/7e2Gicb8q+yDMrrU7E9XUcHwAYVTJKxTjQjM9FKjm7Lp9+aog/6FpPg1nEmDnSpin2xITWfrYUrsS0R2GAiFiYxEXOJKPEmQaFDSMmY3+v4ezlEnk9Uhz+06ZvE7/uD+z95QfReZkXhTV0hAisEs+ZsYf9LgVG4IjMai77R9akhl9NNajI2T/LW6CEm5kTEy5DV0YOGokwUrDWdsMn1nQTsHn6TCaz42E0GlmvuWP2h+C4wSC9vqGGNS43R+q5F19C/PHj+OmXFQ5PyvnEi5g6bRpatGuP5h064e4hw7D6L/HEdDtV8AXlG4PQkBCEhoQgwN8f/+7ajSkPPeAwnCrN7O9+pUmLUlP9NafqFmgj3FIs3L8Qu9J3IcwzDD8M/8Flp2MmQxL4J+IWubRo3F5jopGrF3a9UlYaKgvf2mduy0yzqx/UFlsOxNWwQLTNKYKPge065opWVtSGBdpm1CYwc+7gUpq1Rcv0JFgBVIgkt2WF8pVv7aSG+CM2uwgjE6xoJ9xexyW+1UBoad3WBYATLSIw4LJAXXctrvIJksMCHLL/iW264ESX/jjRpT8WfrdYVhIkAFCMO7IiLx3KdVqU67TokuG8SAjlS2QE+aFTptNlzsxT2Hf3KPQ4d9hxwTrUcyhv/byQSIQU5/GW22H/vxuhcqPW8K64It6o055ndK5dD+wcOA4AMHnD92iS5/yfmRVKbBskHUJzRVZ4FJKb2+5mBx7b4VjOza9gcmrrRnQbPU5wrKy2X5A7YSRXhJZX43zbbmiWdQ1+laWSc40qNTQc5ePxL7+J9R+86XgdVZ0JH3MFWuhTJbclZEAoaCssIg1FG4MJUTZPEUXRol9FH2UlPvld2AvWODj1dGZOehhqAW+pHa2X7cb2ncWv4dr1VLRt0xovznsOAPDpl1/h4sVL2Lp1K6jqClxLTUdNjevwKjff5d/de1BSWorxD0x0LBPK72mI5GHimfkPsCt9FwAgT5+HBzY6ewLZmx1KoTHxvwL3nKNxtfSq4HwPA/DCfmENkk9/suW8KKqdd7WlNaVYd1XYoqcoChQNHGndFPl+XvwyYgY0bP2HUsJsHok3V1pQrVLix8nzWfPWj54KQLpqp0ZG+IVryGQE+KBARLHWCqCSU1UldG5LDvVHeiA/KZe7okGtAkCLXqpdGTPF3jrUaFSgQeFq7cUQAI51HSC9bwY1iYmwUBQuRgaxPHfuwtXA2XaPsy1Cdngz3nzm+F6BfCA5iaG2eSLLGc+FmlrqT9p0TNKeeMLlPq42b+94vuqBp5EX7Gx9sL/PCGQ0kX9TIYRVIOcJAL6fPV10nb0rxPtmOXAzLkKDYiWdA0Clpw9W3v8URnz1O7bfMx4/T3qONZ4TwpUPAI534X//mndle0n7Fx9xNLSUQqhZpZSHpC7E+dtC4sMjhD3R3mqjy/1qhBINbxECwm3n2+DQMKjVauh0OodHJTc3D127dkXPnj3RqV0MBvTti+FDBru9jz/WrsWg/v0QGSF+A9lQ3HTPzJtvvom33mLLcrdt2xaXL18GANTU1OD555/H6tWrYTAYMGLECHzzzTcICxPXF7iTsdJWWKwWKBVK3LvhXsE5d58LwJE4292dxsw2Ziq1agRWmjDr4CL0i+RXykgpxNoTXjOP7QW62k64z+19DuVGdvZ+ZkUmmvo4VXvtFyiDwF1Emwy7h6Ertg2egCY5qbjv1CIoaSDfz1u0LFmqOZmJU75tpRT4a8w0hBbmYNDRfxnbsKnWWhQUMmurbwYnpsKDI/mf0DwcBb5e6JKWh8jSWvd37e7LdBqcadEcZ2J7IiLzPLz1lWhWXMHax4nOfXnHmOvvjYN3DRN+bxIXI9q5axzuMRgpMe0cYwd7DUPv0/sdc7Jd9DnKCvB25PQI78v1RZFb0ZPYtitG77UZt5We4obd5qETcblVHG+5lVldIXntEj629CBf2M07IeXoMy8+j/4nT4mq+0pxtn0PDD9ouzM+1elut9fnwvzsmP/X+sJN0HfFxpFTkdysFeaseB86gy2stK/3CJYxauGEDH+fMJu3nQrvuokhCiGkOyPVjbsudAmwhbo6+edhR464zlGgVo/sauFQuFqlxMxJDwuOCRHWoiUK0q7DKpBYHRLTHDVVlahgNJGVQqVSwjckVLIUHACUKv5596knn8TUxx/HqVOnMOSeQRh0dx/07NZN1n7tZOfkYt/BQ/j+i4bv6C7ELeGZ6dChA3JychyPQ4ecKpjz58/H5s2bsXbtWuzfvx/Z2dkYP75+7tu6UF6Yj/JC6S9FZUkxu0fGTaLLb10AgNeHyU6bTPGTyoF2zhPUb5d+4+UlNBX4HQWV0yzF3QSGzsypfL7s+qj1o3jLhIhNs2LsMdtX9EysLWEyKyLGMZ7jJ34xtt+ZX2jThTeW58dOHE1t2hJpTVvhRJf+rOXlOg3Sgv2QEejruLhdC/V3jAdW1Oqt1CaRXgl3Jq7ZP7bDbaLw0ePP4+/BY7B56CTW9is8NNh792js78P/PM5HhSIluq3IexO/GDE9MfHdBwqvX/v3QlPpJNYaAeOS+XWoEmidwEWvE6+esgicRAEgLbKFoCEDAFbKaYiKhcxSQv0dCsNcEpuGoCbpimDTScApMijXA8SkxN+1TlG5t7z8Lxpsz49FxEtTF7KvXJI916fagKsxbUArFEiOcR6PQeu+eJrQjceOwjJsGvYwq92Dy2MylSPYxE+0CzHawpsPZsvXimnmWSo6FuIh3NKBiV6hRY2EYjVFUVCrVbIfKqUSSoVCcIw2GVFTWiJ7WxRFQefj2oBUCGhzDRs6GGlpaZg/fz6ys3Mwcep0vPX+By63xWT1unUI8PfHCDc8OiWGuufO3BLGjEqlQnh4uOMRHGw7yZaVleHnn3/GZ599hsGDB6N79+5Yvnw5jhw5gqNHjzboMVitFqRfOAeDnv8FtphN+HHO4/hxzuOCNf6ATQjp+1nT8OOcxwXHG4KjOUeRVCwey3dFflC446Rh15uxU+btjwtturDutmmaRstM9sXoxXW2O4bFhxc7ls3ZYkXvJNqtAsV8fT6KqqUzbF9ea8WZaL4HzqygUKNSIl/Cs5BYW22ybfCDvDF76ENfe7HmuvPtybf2+Puaex/Dj5MXwMyZ15ITxWOK8zXPc15o9bUeiKzaO1n7yMG2UUiIk7iDF7mgWpTiDtWj3YQNGCb2C7WcjuC8dRnPz0RLd8k1qLW4JmKQAYBeJ/z/+3fQA4LLAcDIyOUxqYSTaJMigrCnQ4zoNi4+OB67FvETmJlUujDUaPCNl+jMZOHJDAoD5XmUr7TogJMMDSGxkNONhGngccOHgOsMpnSBsNu089eR1LKT7HYPFG3F0EJhefvxuZvgadYjwiCec8VlUFgKvCRaEhz264IPYx7n5ea19bXd1Q3p/j8suvc9pEfyxTjrQkmucBsJACjLl/++7FAUBT+19A22h48P1Go1u/M2DYSEhGD69On4388/4a3XXsXvUnINHGiaxp/r1uGhB+6HWi2vdYGFpvB3ZgfZ++BySxgzV69eRWRkJFq0aIEpU6YgPd2mqpiQkACTyYShQ52Jgu3atUOzZs0QL3JnBQAGgwHl5eWshytO/7sZR3Y/jo3fTucZNMzXGYXXBddPO38GgLAgUkOQWZGJJ3c8iQc3sy/OcgW2ssKisOKhufh+ik0V9a6L7LukDSOnYNvgB1l5FWqlGkFlHB2O2hPa38l/w2y1JfOGF9Mo8vLAv3EtkCqzr86QtUPw84WfJed4mBhhBcaJNCEmHCaVUvLkmRMgkZtSeyIucfTjYW8pr9bTYr/zT2/aEuU+AcgJi0KOi9CMnec3iP9fXF0oXbHyfluZtt1bxYR10hVJhrR7rbhqw4B0FZVtXee4fX1uZ2j7+zvTQbzbbYHERb1MQsjrcG2pOg2gyov/P5bj+dgbG4PkMPHyT1NODk41jxAdB4Dz7brzjvMgp+pICFcXfFOtoZoVHs1aXuUp73sH2ATzRPfvZiVUVpgzFMc0JIWMGb1H3XsCVUmEHJl0rLgIH5FmlAAQYBJPjhYixEMPT6XwDaqBUmNCl2X4PHo6zrXvzt6PxhZuu+5p+3wut+zEW78umGoa3rOv8pX2GKo1WkQ1bYJTZ88iIzMTRcXF+GjpMmzcuBHJycm4dOkydu3Zi9atWsre56H4eKRnZGLyxIccy8pd6MfoLfVrYnrTjZlevXrhl19+wfbt2/Htt9/i+vXr6N+/PyoqKpCbmwuNRgN/f3/WOmFhYcjNFU9uff/99+Hn5+d4REW5lkdOvfobQjqVIKjrKXz12ES2MBHjh/vwpokwWQS+/I2gNcAko0K4uuVwlnDnX7uRo6wtHrLrlBhr3cMts50nR7NShYLa5MXkmHbI9fUEaEABBQaf40hrA/Cqtn0e+Xpb2C24AjjWqgloinJ0cpaLUHltYCUQUMEeyA9yJpA5ZORFPBf2C0JusHBCMQUaeo0KZ6PDUBgQig2jprLGpZKH7Xk9SovwZemUgCeJy8F2/MRXJtVaHYxqjWhOSpWXD7YMfhA7B/CrVgqCI5AXFC560SzxDeSFj5gG0I5OzrvnfAGBPXbKFAWDWotvp72ELUOd1Qp2sTopD9K/tWW77nKlZUeU+gTgukipeblIywMxdvYfiw0jJrM+r+R7XLvFj3XtL7i83MsPaZHiib80pUBuiHii+56+YwAANZwwzh/jXCck26kS6WYOAJcP7+ct86rhl6FfjWmPUx164SRD1G/v3aMl9/vNjEWyjxEAMhghY7kwy7OF0FrdFzQcHSns7V4a7TwvlPhxk+BduzXtpdtyKwgbGnvyMe3h2sCf/cRMKBVKDBg5Gh3v6g0PnQ4vL1qEuLg4DBsxAgqlAt8t/Vz2vv9Y+xd6duuG1i3ZBpBQ00k7dD3lpG96AvCoUc58gbi4OPTq1QvR0dFYs2YNdLq6xc8WLVqEBQsWOF6Xl5e7NGiUWvaPIHH/LvR92PZlZtbrP7ynKYofzEdYEDtbX+oE0hAoRO6yy4y21u9ReToMSQhFfIciJEVXwlLbEG7SXl/eXTC3RPLzJ990PM8Ji8Lhdi3QPCcd58NOY7DR9rPNCW2K4OJ8HG8ZibmbMvHhw0oczrYZUqUu+ntM2WPBuGM0vhyrwMGO7PcRVMr35HgagLjrzs/coNHyEgxpiCegHukxGAOP7UBSy46C41U6b+xrb7vz3TByMmvMoNY68mPEdEyq1SpHE8kajncj198bSHPfHWwnqWVHxPewCYwFlIon+l0SyAWy8+tDc3HPka2CYwd6DUdAeTGGXrB5GA93vwdHeg6B0mzC+G2/IyYrxZFsWi5Q7XOwbRS6pebBv9oAs1KBpJYdodd543KrOIzdZVN7tXtmUpu2Ej1GMeVeOfw4RTxEVOYTgKa57J5SNCikNm2JCm8/xF1md10+08EmzlgQFI7QItsNUjWj2o2GTRNGY2Jf8MVaFaTEtEWmxEV6y9CJGLfjD9Hxc7E9MeLARiS2ZSdbVnnJT55NPLBbdGzrV5/yloVU6B0Ck3b+HjlFch9CnhnAdaIyDQoUaJgVSqxmGGjuXPD9zOLe7zH5zgT+hE59YFRr0efUPsntBWj5WjnXmrXBumhns1VumI8CUKB2euaEcqyyPMOhos0Isxai0NB4nazF0KnsHcj5Y1pPdt5glx49seUvp1qzKioGY59dgCCNCsFmI0rzRFSHRfjmc+G+bXVtVSCHm+6Z4eLv7482bdogOTkZ4eHhMBqNKC0tZc3Jy8tDeLh4qZdWq4Wvry/r4YrKEgnlNgAADUQBlMqK68f5+Tp2QSqgcTqXihkzCiigsDj1Yvok2u4g7EJ1QeWeWHPvY6x1Kr1slvqa94U73v7v4WfROsMb3lsOggKNC227YeX4Wfhj3BOo9NCgXaaStY9cf/4P1f4ZUDSNccdsz5/ZzL8A6Azskyi3cSQAnmgYYFNrNYskkNpj88dFSpHtqroAUOnJ/m6U+gU6lHPFbhQKvXX47Efbe68W6cQsBM2oxBH7hhQHOD1b9cmTONxD2LtwpdbAK6s1VOxiZhaVGmtr20EIFaxZFEoU+wWhRqPGkTZNWcvtGGr7KhX5eIIGW/X3RrF1CDsMu3nIRPz4yHz8de8M/DvoAeQFCZ83mBejM82cxv/O/vdh2czXkckJ+4gZMzQoVusJLia1Bn+NES+tlqJKIIxzsOdQbBw2iWXYn9j4F2+eEBkRMdjR/z7UqN1P5hUzZs6178HLL2FiVShAg8JJbgVfA58yaVDY03cMDt01FOVewp6JoeHC8hMAsG40u2v8qU59WEnKwf416HT336Lrm1UqlKp8UagOhIzernVGJxIis2OlaagFmkV6+QdyXgdAwzBoc2r1voqMZihEkum5BpEsGtFJdcsZM5WVlUhJSUFERAS6d+8OtVqN3buddxpJSUlIT09Hnz4N19MkP/UaaNqKk7gLP+NpaII5wm0WC/RPB+PjMe/iwqN3wVgt3Sl263Xhu+L6wDSQmM/PF57HuEN8t7WFtoCyAuU6LfSceLv9xC0kPw/YElYji3QILqNxLTQAZ2sl6fNr3eP2ip73jtl6eXA9GBQNDF83HHvT90JZm1NmUihgpYAWOezJSgt73f3tm8GoVGDOP7aLRaWnN87W3j3byQ5titQQf/wjkNwLACY3lGXNHCVWK6VAclggDEoFzkexE1ztWhsUAK9aR56c8mQhtnV2HX8uE2icKReji+qQEk/xce5dZo3GA5899RZ+fmQ+rtZWtFTVGnHMiiSjxvlZXmsmnvh7qmNvyWNrSC63jkOZn/NzrGT2uGJOZLwo8XZ6hM/W5v38cf+TrP+12MUcAFQW4ZuE+sIN4xT7BeFo90G40rIjMiUMR59q4dDL6nFP4GyHu/Dn6ImC41IwK5uY7Bh4Pz59+m3R9UwqNfb3Hi4rv8hOuK/7JdfMEn6TWvh8EOlZjh2BfVCgkW62aOfy2Hvwf23iMbX5KWxqxa5APBfbE0Ylcz+MQgqJbdY3BKWUyOK/5tsMFyurpaUcaBomqxU0TcOzNpSm8WBHRNQeOuhqHQLLvvkWLeO6oFXnrohq3RatOndFy7guaBnXBZMfn1mv98Jl4hvvuzX/phszCxcuxP79+5GamoojR47ggQcegFKpxCOPPAI/Pz/MnDkTCxYswN69e5GQkIDHHnsMffr0Qe/e7p8Qd37/pWAC3G8vPYuIngX4nHoJe6jhOD5hEGu8oGgXVsD2j9rqcT9O/LkaVquFtx07R7KPuH1srliw3xk2s4eQAFvfI78qzo+VBsxWMzpcF75DvNC2KwCb/LwUytqPKocjbsZV2RQ6sedW5eLZvc+i83UaVRoVdnZqju1xLfHBLxZEFjnnd0jnr5vKaF+wYeSjvPGV42eh0kOD1GbC2g9FgWFuGTRM7D/8Ih8dKjmu9/jug/gruLBl6qJXciNIDhc3lLgG2u/jnX2h7EnH+2vDdMx8MjPjZJ4fLO453d1PWP/oRpAdxvwuM9+n64tKQpzzBqpUxNDc3X+spH6OHOSWYf/8iFMwcvPQiYK5ODUaDxzpMQRFEmXjx+PuwhUR40SIlGZtYBUo55XD/t4jeBIIAL8Hl512vvlQmlx0mxWA+btr7i3sdd8UPgTTOn2AHr3XomdQhuMbENRC+De7M6QvdEozQj2q8EkMv2o1JUbcgBejrjdDdlSUsKFHUYCZUsNCAyaB1ipm0LhYWY1zFdW4WFmDjBojPLy9ERwVjYCISM62KPiFhCG4WTSmTX4EuzZtxMnjx3HmzBmcOXMGuzZtxK5NG/Hpkvd4+6kPUbHuJVXfdGMmMzMTjzzyCNq2bYuJEyciKCgIR48eRUiI7cf3+eef495778WECRMwYMAAhIeHY/16+ToCTC7HH0DqWb7uiUpnZhV+7KTYVvexY1+hknIaBtebtoJJQtrZXuXTUJzIPYEyQ5nj9fqr/Pdv0qlxvX1rmBVKULStf1JMjrAb8HjXATgdK15pYqdDqvAPzd4fp2eSFd9/YYaCY8yMOOV8/dJfVlyOdOqZ0ACW/mCBf6VtTqmAh8DMcGvmhvKFzeRwUqrkuZYygWTRv0fYcgXsnbSZ78yucWNfRsMWcgKAI93vwbl23VnjJZ5abOV4j6q19e9BUl/sbne9SKNGo4p9WmCWIHMbAO7vPcLx/KfJC1BVqysj1TfJXaji+neptnOUYZCySo0F5nKTjFkJsBLXoCKZ5ddiSCUI2+EaPFVevvhtwv/x5u3qPxbb+4/E/zgKvVw2MnJkXF1ghRLP5XJOoALPjpBqsN7TG/Ft7nb75oT5PW3nJ9wp+59IW26aWaHCwmHv4ocpC1Gj8UCrMPeNJwDwCHEeo4/a+Z1VSPwYfF2UTbtCqxC+1ngHM7zKAv/OXCvFMnJKTLYbZJVGA0ohbBYoVWoE+PujeUw0Wrdtg1atWqFVq1ZoHhON5jHRiBBI/fANYXu3y0zuhTSf+vYXPPqBPNG9m27MrF69GtnZ2TAYDMjMzMTq1avRkpEB7eHhga+//hrFxcWoqqrC+vXrJfNlpDgdexcmlimQY3Am85lMpeg4jR87PbvL2T4+zcg+ufx17wwwL3NWqwkKD7PjQmUWqnaSidFiZHWlrjHX4PF/H4d/hRcGnwxFQLka7xx9h7fejkcfwF8Dp+PIgCFonemNx/99XDTnAwB2DbgPgNNLw4UGkOfL1xGp0nkhx98b9x+x4oX1VgRU2XQ9MsOjsWXIQygMCEXPq9wfr/N1em3pdvNc27JKnQanOvTC6vtmOnIupFRn5XJIRD3XzpnYnvjh0YW85VVePiwDRshFa/fYnI4OQ3J4IAoDQnG45xCePkp866a41Lozaxmz03NDCqG5gz0xWkj3A7B5Xcpqe2CViigs27FyKpa+mb4IxX7BcDs4LhG2Uea4Fi8TwiJyUnbskvG/XfHQXFZuhd7DUzK3RW2W/xunyoWbVorOl5FzJ8fjt3noRN73T842rRKfW6f0PFTIFP5zF6GS/LeHLsKffR7B0ifeEKgoEofZkFTs0zzl79Q0SfePQrmPP87G3lVnb8memAHwUhkRrNVDzWhjUK4ULqsP1FRDLdHIUg5UuHCRgxQqjRZmF+9RqE8uRVEIjGyCgIhIKCUqFZlwQ1bu4hMY7Gi74IqbbszcSA70HoEMWoEl15yZ2Vev2MrNToCdl1Fd7vSE6IP4bmMLIy4ef3wMTk6/B1899iqq7gtA1M7LrLmF1YU4kHlAlibMwD8HoteqXqgx2yx2g8UAq8IPVzt8h0N9nrXlx7CutrY/F5U2xdTTre9Ch9SWoEFJGjOATX6d2QuHSX5whOD630y3xe0n77e9F4NSgUM9h+KP+5/Epdad2SWk9pMy43gTm4bApKCgoAHPGtvA7v5jkRHZnOVNaexiRum7S+cbF2qGeD3UH3q1Crn+3sgJbcIzCM9G8Y1AoW3LVYNtaOwGVU4I3+tl/9ztHbR5SdaM74RYoueuuoSRTOK/DWWm+8aMUaXBZ0+J524AfEOVWV0jVgnnOCazfO+rj4xeYEyk8igcXkEXxgwNiqeizLxICxlD59t1x6kOvWCRCCH9NcL9/Bq5JApU6BV4O72CP7nwLjFh6y0Jf1Y1KmEvgVQCMwBc1YlLKygpa+3unPtM1TWBidPXzU9TA43SIhomko1SzGMl/J51vr4Ialr3SkKNzhNaT3YiOtf7IpeQmObw8K5fSJbJHWXM2DEzzM70i8eRhSZYSr0oPNdkgsqbf+KymGzCTfn6fNToU7Cbsrnbt0WMQ/v+bE/PtBXjse+VJXjwvb6SBo2VtqKydrtMXRmDp13K35an0OOyv2MsqIx9sTEqPfDDowtRFbgAXjXSJ9EdElofJzvdjQJfLxgFVFaZVRXVGjXLdV/j4Qm9xpkoy/xrp9hbB4oGhp1in7RzwtgX1yo3KoUakiqGBP8xjqKuvRpmX22+0e/jZ/NyALIDfZAeKJyvxDQQ47vfI/uYxoQ0nOFzrTbX6LpAzhHzImmlFLwTO/OCKHbBpxUKt8NMqmRh177yekWd7pNTo8TLwu1c5HgtmJ4BsbtzuzYKN7Qqxe+9xPv6AMA3nNw1o1pc6mBHrRHODfdxEdJK2naPsw2MkB7PjoH3Y3f/sTjVUby4YtNAaY9nfbgW3Q6GWmG+Go2HI2zrgPOeS30CkPD40wiexC8jZ35vg7R6BGjYBnGFgOCiDRpWF984g8K1uFupir39Yo9AlldWVysARlGAt7rhwqh2mN9OCkBQ02bwDwuHX0gYKIpCtUDvJzEsLr7rnr51OzcplSr4BrunSybFHWnM5KY4u6Dqy4uQD/EY9+Ftz0Gots7umTmeexwGOL/c16lWUGnZX5QuCR7wMqjQ93wQdhxbx0tCPpF7An1W9cGwv5wnCnYpNvvL1PG688vD1GNhUu3dBTRNochfuv+OGBdrvQ2/CTSMW3vvDMfzYm++G9Gu4RJZ262giDGHBgWTUmUzZk6zP4frzdo6QnU0GEmmAgjluzQU305/WXRs87BJomMAo+9RlPCPdNOwSQ4hskSREJ8QX7gQ2pvTTP7dUUFwBAxqLYoC+eswL5K/TZiFFROfYU9gGDtMoTwuGZHulWVTRpGTq31/1e7lobkK1dCQbpsg5vlwem/kGzMdfaVd7RrOvtaMFW+JYs85uSTSu8qOkDHG/L5JeX+y66EBVF+qdDYjYNOwSZL/HwDYOvhB7NFE4SV/fvIyK8xEKfBIzFnW+P7e/I7sgE3o0ZVnRuqzs0KBKoUOBRp+grhJLWwEie1No7DAS+U6RKlzIT1SZraAUmvq7AWxl2m7i1Klci0m24AFEnekMQPPJFw6asuJ8WtehHIIWZa2L2y1Yg+sAh9TebUzDCW8vhMl4zyd+PkKxK9bzRp//N/HUWmqdCjqAk5jhqIoME+cSS1scV57QrBXjQa+nYTvarcPnoL/TZoneWyuYOqe2LGrBQPA5UjhOPY9Z62YdMCKfB9PmFRKR1+jlQ88hZlvfgGa0sIi8O2z3x27CpHVVUG2vjBPSNkCycnMi6BQvklhUDj29h3jtpHppVJicaS/6PhjTdzb3pUWwj1QmOGHfAEFZVd5KHauS5Rm8zBZQVUJnzApoy33QJnnXqKkUeTCYSdVREEYqG3P4OIk607ZvEZEp8POsGD3ukkf7ToAOwdKJ+FK5b0AkLTF5P6P64qnQVzawu5RSpPhWZPSMWIa5VuD+/MMkDIff8H1yr398Ue4tNLxmrARkuN6pbSIKAt/8fcQoKmWTB62I9QoknlrkF1jQgqjPN9dHTQxL47RakWZySy4PY2nJ/wjIuHKmhEb9amDx+bONGYUQML+txwvf6Dm8qZQKhrZVy6DUlhRDf6d1d+XnAqHP2OW5O58K9lxzfi/Vrk8RGVtd2DbF8X5Zdk0/BEAwBenvgAANM/zxeK+wtne16PdTw6TS4VW/GKh9/DE7K1W9EqicbJFBC607YqlT7yOpBYdkBMWBaNGi4GX2+BKBP9i/9uE/0NSiw5IiJHujSPnZFcfzrftJpigy/zZHhMQ5WNeBPOCxd/DnxJ332LMaRsjOqZ28w5nOyPkwCRNQrUXAEp9XSdhiiUWi6E5mg9FpbDnhaquTaR08wZu65CHJMeLBIx0ADhw1zB8+dirKJVINnUnQVR1tRwqF8aB1k3jQY5GS1pTaR0jbkiXSX2rsZhE52TyluklKvr+cfF/E4MrVMj0rnwWMwNpOrZhnh0ubETQFIUtIYMk97UzSLxSkqYo0W+HQsiCpChRg4WiAGjEe3JpawsRvPz559FMsM9dNQyDxN18RL2IMXOpsgap1UaUmS2sPBpKoUBgRBOoNVrRxH6HkJ/IeSuidTs3j/IONWYo0C6t06Z35+KPxQuh8rBgBfUkb7yD+mDttiikIYY1ZoYK+36zNVGkaRqKOkT9qdp/cm5VLu/bZ9BoUZiRCgBQ1KX1sUyOdRFW0AWAUy2ainbJ5irXbrtnAmiF0mGI2anSBDpi5Ew2DX8Ehb7uq0uqafeqRqTYfs94h1ggE6asvFAoI7VpK1knC3fk6V0xoaYYYfVsXmlnwyi+rg+TKi8fl14PIahS8f+NQi+u2aTIlRaoFEJIKZeHyO//WLeBMGo8cFpC2G/9KHaOBqUXD4HdbVUDOWdFxx8KlyfY5g45IU0EE9eZSIVLG7paafXeBa4n1SLkDZTD7w88zXrN9Uw9F/uqrO24qhIrU3ojXyCEZCdbGwIxc0GjNEOjsMBfY/My0gCqaQpqrnIoA5WvuMfVN8g2phC46TJJXHPyjHwv6MwxIzFv3jwAwjkyVonrZaXFCu9Ap/FfUKnHuHHjEBwcjMDgYNz38CQcjuer5gO265yQkrC3gIHmijvSmAFsN3pZSZdEx/3ai3dmBYCDsCWG0gJW6w6MQsKWDQCAf7+TVyPfMtML3nrnl9IeZtqXsQ/cH8fO/vdBW2DEmfwzuNZEOpeiPhzoLX4HGB/XA8lhwl84mqJgcNHV2s7q+4RVI8UT9IQJogvQDSfcWscVJf7Cd+f28JGQMbN+9DR8Mutdt+9+pC5qL8SISxFM2vgTFreRznGY6WYICpC+ezvdoReOdBvk1vbUl0rdPgaqwnlKlirP1u7IgqLQGYa6LCLAxtp2PVqOXItm3zVqDon34nplTHsg76Lo+BdhVuCPR0TH3YUG8PuE2SgUadtwo4kozMcgRYLriQzE9I8A4ERcX0GtpkqGAZbUPBbnODciZ/yceTVXmseKbp924SVr2+8fVKqkjWUDJZ4bE6ithofSDAsUSNFF4YpZg6ueMaLb0np6wz+YYzxRFNReFHKhRq7B/Ru4MpO48QQA1wUUo5P10knKFMMIHD9hAsxmM/bs2YOEhAR0aNcOU596GvkFzF5zzvkeAuf6fpOm8Za54o41ZgBg9esvwAzhih8LlNB4i39RvqOexa4rychaxW9dkAWn+FPivl22JxSNwLal0Pjyt9nvbBD6nwvGg/uaokWW7YeiqP3XWGkrvDg3p5dadwYNGh9tfA3/e0B+uWJDkh0WJakieykySFbfIrE7MbNSLdl9mIsKZvihzPVENzCphGPf9gRohURlWm5oE47arDQftBF3+z8TLZ7c+8q8BQhvacv/8FUJ/5xfb+Xe3W5hQIhktcyB3iNw+C7pO38eZtfVE9qdWazXVJXT46EoE8mpKTPaKqfMTuNkj4vScFrGHHeQSmvo0tQPsBhx9JiwwUJ90QVIarj2J4ltXCeVu0pwdYdPkz6Cv0m88eOy7A/c3uYOCdmEfXePwtbBD+J0B77opz0vb9OIyaJ92QBg44jJomMewexz1piD4k07xahQOUNDNE2DtuhBW/SwWqphsdbAaDXivK4JKgHQFj1qaDNMlBFWaw3rYYEFFoseam8da3m5hwoZWn8UVJcjV1+OGlMVlEoDaw5t0cvOjVk8+ykkHDqIZcuWgaIotPbS4fLZM1j0xGO4p0U0eoUFYWhcRyxfvlxwfb3FCqo2L6youBhXr17Fyy+/jLi4OLRu3RqffPY5qqurcfmKs/DGJ8h5g6Xx5Bunag/3+4Xd9K7ZN4Mj1ACMp1YCAP7BfYJzLFChSV/p7scr1q3AEJwDBXZVxz5qGLoqD6OqtMSxrMWoDPhG2VrCn/m+PYqrixGosxkDrbKcX/4BZ4NxrUmV47UVVvS5TONvzrUuuTQZ/c4FY0/jpo6Icr59D4zc/zfiBe7Q05q0RHRe3TtHA7Y75zX3yc8rUcKMDjiPHRBO3vuRnoqL6IjPqZcEx99r3QSvXmVfTEv9hI01ez6F1N29RaHCyS795Bw6AMBLQttDo1CApmlYLFW8sdAYp8EnFs7kVsu4YvnDz2H+j2+6tY5rKJuWjJp9IVUUOT0qPMkNIeUuic3LxZWGTIPyxyTgynZOIJpP26prSPJyL9dIiAO9XJdOX2wtXQnlDm30qZLVPdEe7p8Hrookp9u5Ft0W16L5CeaXWndGxyS+wrud1KYtEZOZIrnt0wHsffe+cBr/9B8iuY4k1moUnbZpmBUBEGttWSSw7BIAXBYY4HBYaGEGENT1GKB0ernMVhoqBf+/9eIHHyMtORl3xXXCO++8g8QKPb7/4H1cu3wZX/21Af5BQci4loJgkZSGaovVVmYdEgrfkFC0bdsWv/76K7p16watVosff/4ZwUFBiOvo/N2pGH3clBx1594T6uapvGM9MxE9C+ETVYlTFn5eBACYoIJfjHSoSVNpgcbHKFjttOLBOfju6amO13ZDxs605ffz1qFBodgvGLaU39pqKlM1YvL4d7WNLSonl0MCd+jbBtuE+KTuWF3dNKy6n5+nJIUKZnTHcdFxT+jRQ2J8qkBVVkZkc8l9UiKdkwG2Vk1DsHdfe+w/0Bnhav4Hl5b2A+KPDsc9AcKeJIqi0FznXp5LdT2VO/kHIbxYfVq8W73qunxZeVWyuHeAy2lO49L6MrV3tHjezJXtwss5KGUIaspBTi5WjUQYx116lCeKjvlVuP6fPLta+G5fEpHPytZ+QtyqXXvvY27vSmWRDsncTlhFrho+fn5QazSo0WgRFhaG4LBw5Odko13nzujQrRuaREej9z2DMXbsWMc6JSb+993T1w+evn7YtWsXTp8+DR8fH3h4eOCLL77Aqv/9DH8/8VwsZpgqsrX7Pa6AO9QzY6fl6AwoRdJY/49aju/p6aiBuLtLF2aA2seECor/T2KWNCu1FlTBC5txP/rhAJr0y4Wh9obUXo5d4eWD76bavAaepWsdxsyKiytwn85Nl/4tgkWhwEoRoyQpIhAhheLGorsJso/il3q1bNu7rx1ArZE9P6VZG0nhtE0SrmwuHbylXaqVxadB07Ywi8JSCoAh7kZbkJzyIQDgCb8/sQFjBbYA7OrRFi0Pnpd9TPbvolyaeqiRWSOuR0FVmwWvM5TF9hl6oRpmKGH30yiy9VBUmtE15Bxi/NKxIXkMFLnVsIazjSxFkS2WL1YNJUSmCyPVXQa0CcH/EpJh8az76VTohqjRcCNfKOnQGAzu8BOyAoQr8ygAbyd/hWfa8xNsP//8HWAgfx3WodRBZ4SihaUbklp1wr2710quG+MlbjwLEVoi5DNxA4XO5iEBEKhRoUnJJZQrvZDmwQ/9hhbalOlDPSpBUUBBYCwKTWa00GlR/f/sXXd8HNXVPTOzTbvSqncXyb33igumGGNM7713CJ0ECC0kBL4kENIICYSQhCRAgAAhpleDe+9dsqxeV6vV9p35/pgtU96bIq1sCHt+P4F33pS3szPv3XfvuefW70MoJj5fHQUlqkzLiWwv0NOIKM9gV86I5LVJCFM8nrwgIBBfoJ13zbW457JLsGvLZsw97gQcd+ppmLz4+OS+dQEyBUMQBNxyyy0oKSnBihUrkJWVheeffx5XXH8D3vv3GygtIYfM3cUlaGhqJrYZxXfWM5OAlov0GdyLR/FTavvrEy/Dp0OX6V7DWenHQ/g//Ic5Gz9gnkHx+C7w8ZcxHBMfinck2QX+vPOwdfWnAIDpzijCC9OvEKmEReh7PSkSBAAbxk6kpkD6skxoMcTxsOV3xO0zhNUYj+2mzycFMW1SA2+ecjlqddJfjWJCdhaCwSZqe+g/KZK08nn1dKfc6sHOD6nncFk4zM1Lr7dICk7DlLR/3KgOIUnbEcYOxzXYZpd8z/iAe+vUF3DqsA+xoHIVWKhXyVyjhBhMGaS5Om0Pa3+x2L4Dtg51CDA3avy6tCya2+pe7nO/0oHcmA/T6skE5tNbxTHqvNYP8fF6NZGf4/W9GvklPVi8ZoWpPmm9q3rG0TlD6J4kEoY1HsZ4iciqEsvvuBJ359K9ngzDgOGcYDgn/IIdbM5gcKwjuU36x7IOsKwDFs6BKJeDlqgVMSYLzVELLKw92c5y6uM592BwucNgL5uU2t4HQ3FfrzjXzF+8BMu378KlN9+KtuYm3HD6Mtxzj1jLrp2QDZXAp59+infffRevvPIK5s2bh2nTpuH3v/89XDk5ePM//5XcGPlxshpOfRTS+84aM4nXYTdDj8/uYiagi9HW1fg3o6+LsHrIsWhh5CubxKS07SPRDd2sqBi78/lX4Y/4cVlhGCsLF6nOuWCr+QyVJcJ/qW0P4mHT59NCV3YWWnK1M5IMpdBKMCryKXG7DelIyTYfuPOlKYX1rNhf8fVKMr+mgvNj8+AUsVn5mm/cmDKCg6FGXKEIl11SLvJ+gsFG5AxQGv/1g4pJItlJJLwvXJ16wgeARSWrsGpGHg4Nkxi4CsPkyvGvYGbJJvXB0t0oneAa+laoMgHLHh1i+d/Owu86n1Ztfmvz9wg7kyFQhuLb6l7GqW2fGz6PHowUqFSiMIccLjq97bPUPhEP4VosMFpbgM7lDmHelvWm+qNVoHVppQGSiUlc/R+6xzYrFILjl08ZOk+IF9CeVQrk04XyEpozDfaUB6MnyiM7OwscwyPHGiLq0rRHYohllyBq0Q4P00oTWK1W8IrM3IKiYpx+8aX46fMv4t4nfoY//PGP6InG0EDwwCbIxn6/+K6xiqwwlmVhzZL2jf4c0qp26+E7a8w0oxwHkJ6VtR6Wj5ELlAnx/2xq3YSdr76FXodLVX0YABp8IiH1AEOu7XKQUFuHhnuEx2EB3aIejn04VjDH3H9vEVl4LYEGSn2iBNpMpI+OFnYaCiNxffQwaXnoBhqx1peobSOjq2SfGYJ3QorbhsoFz9wWDoFAA75euQBtneQV8CPD+6btkcD+Bi9q28mGihSWffJJkW0R0/TOn/IG/E4L6gY7UbL3IBhPGJYDar7MBXHSvhz6vxvj7Z/XMRHK0sIp7CpV+KYorB3SyI+kjKSR/kOq9r9uvx/ZsQBe2PkI/rDzUeI5loXNeZ3e13lnSbin8c+Y0a32fCbfmYnnE4UEYxwHnPCI5rmZnLI+hZpoGO7u0mxfmSuvx3X8OiJ9VoaCbo9mO6vBnatScNW6IzHUU0p3lDh8KLaL75E0IwoAuixOFDv8cFnIz3JDMIKmUES3jlIngesCABVDhmLb+nVoOHQIXR3tePbxH+Oz/76LugMHsH/XTnz5wXuoGjUaB3VStOfOnYv8/HxcccUV2LJlC/bu3Yt7770XNTU1OGXpUs1jbY4slAwbgSHj+0ZQ/84aM/cwv8XDzM+OyrV3YBIKfTY8s+EZAMCqGeSCg5/WkT0RIgS8cYrxXPwc9CAHdEIeCwEn413D5wOA7WOmabZrraAgCGhReKO0kAM6GVQ6kD4I9eD5f4J++joD4FLhRcP9OVoQYtoichbFxMAyDPbuE6tH01I1XTpy+3r4cm+rrk1hQRQuyEsScK1BuCD3mlS0HYZ9TRuxVlMx2mD7qgVMZ2pAfdLyPHLgxwXcZ6r9E2AAcCYIwkqwPQaNIcW9twraPJ73NqaUw/9vn9qzc1LHyuS/ScYOAPxplX6YW4odo6fis3nmjimKdOPdzbfQdzjpx8Tvmt/TDdjpCrYAgBGL++QtokEv7XyDW64xM3PnVt1z0hY6D70gaohpceeyLQRBO0o4lGHoEZYGVuLlplzOG41pRmgCMR4xyrFX3HY7OI7DObOn47hhQ2G12fCbHz2C8+fNxjVLl4DlOPzfi3+hntsTFRdZRUVFeP/99+Hz+XD88cdjxowZ+Oqrr/D2229j8mSJIUm4Z46cHCz73j3E8gxG8J01Zo4UOo9XS4PvxWjM3p0icfIu8o/32qfPU88bqzRHNrQhhJPwHrHtGOFLAEA+tFc1ZvCPM64Fr/FQhuwOTVE+JS7HCwCA3wrXElpTb/Ao7FG1DoJaUp0E2v05UrhN+AUA4O6q1DOjHkhTn8fE1OGT+np5dsgguwXt7aLWUSExATQNtd4MOLU+st2LnQ55qv3I5n142S7npFmQmhQr0QYl2N4obBvak59ncXuwzXEt/s9KfleSCsKW/nxJY167LIX736rBGbnz0F9QFWxMfi6OaL9743oPGuqDWRjJOKKhJNwJzLwWyC5FsYKw/4tnfoICbzeQK2pKvLT9AeI5LIUFaS02qEekfnrolWm71qKNIrHXjABjr4lq1X2B1p3sjMTQQikaOXTESPz148+wurkdm7t7cd29P8CbazdgdXM7vqg9jGf+8Soqq6qo55YSgmfMmIEPPvgAHR0d8Hq9WLVqFZYuXSoPH6XxN08gY8wMKAT8aZQ6bv4GcyEYJpV+bXWSH7DjNooZUdMEtbItP9EcgXYw6mBHGL8V1ES9a/AcANH7cZrwpqnz0tBQXqXpmdGrPq1EIUSXvVmDa5BQJ/v8E+Ee1T5Vgqg9ofV6LXZo61P0FcMiKeLvbKzCwQVjcG91Ob5fXYY8oRNnQx6vr0BKC6fga3kICgBqD/1B9nlGz6+T/z4f5JpgDBjsX6CvmksD1xTQne+rWVFvxB4UjQsmGMWntnsxiZNP0hZJ2u0ijlIGwERE0Lq9CznwIzZEx0NAQWXNIWzNuhEsJUW3KlCf7I5LEeJkKJmSuayAH9Smzwt45yH6ilkPBV6P4X3XrLlA9nmmdwcw6mRxYrr8bVy2XBw75m9eh1k1WzHkt6Jxjhu+xMkdX+PCOjVn74RR4zFtd//I+1LoeXkCnDxzcMrenViy6gvivje9/jcAZGPlmC3rkx4ZrTCTGejW/ErUadLyeGvAGx34NHNBEFAfDKMjHMGe3gB296Y8ySzHIaewCDmFRSpOTTqQMWYkePW+m9N6vrEX0ifAjrxCbGoVCY0xhu5leQ0XYSMzU7U9a6g5HkDiNcmHB/ME+cvrQMptfzr+TTx+lEAv/UDDh0eosrXW3HYK3pF9rkYNRghy7819EMMwtMlnqHAQVwa+368+nlacR9x+XuhL2Wcx9Rq4q6oMv8V1Km/KlXgexwqf4N1pI2Hj1cRn6XcY4rChtemfyc/ZIPMrGJDd4VJkaQw+TDAG604PtZ2V9GnuhpVgG/0oXy8aMfUVcsIiJ0l7sjv1eTh6OF7YiG2Oa6nkYD2cjS/hFnrwweYbiO2/rnsYX80pQHuBVfb0DA40IYuXVCoGcE39GwCAH+55hnguEi/FCK5ofLtPxwHA3740/lwPDTaj8YtF+N2uH2PNmvhiJOF9Yhhc8d838MxTP8JDf/oNRp7VAteJ8XBWuRheuKQjZcwsW/EJdn22DKVOJ/J8xvWE9LAxh16qgISyznac8ylZC+icT0VPba5P/d785LkU6VfLM8P39CGTzkVO7ojmVwP5VWBY8nzBANQwUl+RZ5WPCy/84ueYW1FC/Fu6dCl8MR4d4SjqgxEEYwJCMQEhibHnysuHKy8fgiCgPRxBfTBMDLtFeAG/OdSCzV7j5P2MMSOBLaYd477hzddNnc+eSzc4XjzrdlgjDPwOJzaVqKW5ra4IssIc3mbOJR7/HNSVvhcL5DDJRGGz7POZoH8P2oQ+DAPjmUgHzsGrpva/B08k/50t9CAnPskzACoEdUhKi69jFBdEnsaqMWo1VFZxvzs6UoYmafrNhRfX41nMyHWBY9QrrVx4cWVxBMuKc/HFLGOVZ8fq6NzYowKeHaJNEqaJxo3ZvhH/nXgjAnZxqMn298K2rQtcr/hu7B8mz2iThpkmjFJnL5WhAxAAd283SkIdGCoJ1aggCHjQIqY2j6BwThDSXq2eHs8kmujbB9vX8t/v1c9uRc+oboRtLLZMyEUskjJeVq67JPn7NZbasWJOAe5ufxYbVp+LyxvfIl5ruoYAnRa0yOs/3fdLzWNHVNAlAUhgIeCc1o8xNCElUJJ6xsondWHy/t0YPKmNGEWIMqmJccmaFchnxfcuuyKo3jmOpV/TuVAkXDzp56b21wIXN1Jye3tgD8uJr9Kvp8WZiRyuo7Yp0Z0g/bLkorHtUR7IykeIwrkRQC4i2R8UWeWG03lXX4NXV6wi/r3wwguGi1QmsqI6wlFiLai/NLbj8YNNOHkDPS1eiYwxIwHH85jdQC9WWBQ9HtY0uep4zoJLPhqC311JjiVnV2hbpKSU8au6/07MSEo8ShafGJrKhYd6XuXkmsB5+CfOFl7R7FN/MEtYqb8TBRWQT2g3CqnQCmmgz0EPxtvF+6v03Dih9gboun8B1CzUZuAzXf/FwV1qz19RmZxTtXs3+XkggaOIt3y/Moo/TaiGnVEPbPn75cbaXyZWY5pbNCjur6YIo33SiO+/pFMsMELuy4Ojn0BjuQPbxomcisTvwVPuqVTEsopVi2g9Z3saDIDTVv0XG9acB6ugUXF4dzdi8SFumJ/Mm7KvbgN3wAv7Z+RJfVggNRkpDbZsi/zZur1ODEucW5qf7FdHvhW7RucgYmOxdbwblSE1DygB6bv39J7/o+5nBoNCrbr7vL1JvTAyhLGnAflV8Q8MCsf0YuRZzSgYRR67ykIpL+PEA3uAbPHZL7zgZNz0OllPZ1Br/4TUjMBIJuPV76TE+C55T+691vLMmHGU1Dkq0M1l4xCXRz6XIJYkoCHCC/DoFJE0C+XVcgsKMGT4cOJfeQV5wbO3V26sCIKAGgnHJkDgEa3vNu+VzRgzEtgiYRTXeKjtrACctNH4Kn0ntGuMaCG32guvy5yOyYj2NgwnVP9IaFjs+ULUknGAvBKq/fgB4ottESJwIIhz8C/kCmTOyhmCOa+VEn1VQD1X+Kdq2wJ8gbnCCpQKTZgDspH0x0EH8KhwH07DW7LtEahXRYm+Lcihx8ZtCGKog7yi0sJUt3rgDwQaCHvKEeMFDCo4TG0XBAErvlbLr2Y3tss+LylKPWO3V6nJ6gn0hMieF7Y1zoERQDQIGIt4z3py5Cs8HgzCVpJBk9rWla8WI5vM1ojXhaBpyAAAokLSmJF6BWRXC8Zg3d9DzJ4CALuEB5PPy8my03l5WOim+lfx2fqr8Mzqm5Lb9oyQFB3U4XOc1yKKHk7o2YeLm9NTfLKXogIrxWzvtqQAnhSntH1J2FuCxY+l/h3/bhY7/R2pDjbg4ed/hV8+HT+OExdXzhmzcf4n/8WZn3+gOkbP0DhptU4fjYBgjNz+Tzmn6dxPl8Pt68HIuhpc846cx0YrOFva0aZZ0oWE2qxKeGhFWRlgdy/di9UXaD2SnEmSbgcl7RsAwpJQkxES9NqMMdM/RGzFiFJcfCkYv2WPM4/p70SBqzqI5SeQQ0w0MLy2FyHSWwJnswsceJwnqMmgwc5qorqmNCxFE/dajPcxU1ATUo0iC9opx1LkC6kV3hxymTXcimfwNG6lCurZWGAk9qk8UeOgdvUnjBlncDO1T59/MRHBANmlXCrQXfmkUNGevQ9h3z668jQAvLv2NZwwnpK6Lwjo8qxGNKL2Ati49LqhpbWVlAYB0y2/9ztGZyM7norNAKgbpJ5ol7GrAQDjmVri9Ugy9loYyoieiRvrjZeqkMIimY02O27U2FP8TmN7D8JSl3omA1nGyZrjeg9i86qzsXyT9nVU19WYMDkhht/sepzYdmVDysNw62H1eKAf9mIo/ybgtk3AJW/guI2rMWVfnH93/kvxQ1nkj/RhwWa6V5yGY+MZRTT884d04cIPb70UAFDdVI/KVvk7OmOXvPQHKwh4+97r8ccnHlB9U4biLXH7fWnVr/JGeV0dGTMYkmWDW4MrN8JpN9V7WvhLCb3vIAgCGilZV1rIGDMSrJ35Q/BZarKtFEZCDunAlcyraC4yJ2bGSP4rhdTrIbSKqZJOkF3BJM6M1CAoJKTLAmKW0flQe0mMYhE+wV3CE/o7Avg1UoN9YrCIBMx5sRjK73gu/okrhBdk25ZBJFiujfatRPksrCZuH+qwob7+r6rtHR1foO7wnzTPme2nh6NW7GvDqv1kjsi8CvXgH4zE8OXeNoQIIVTbKu0wReIuuhTG6P3ufNjWyJ+V5lIHri56C4DoWWkrVHtejs8Ti4FezpFLMwgQeTVGJgmuIwgnI7q4F3rUYTLLjv5JEcT6OXqGrQw+m1eIHaNT3puycAdsFH2a6+rJdYe07oXAMDin9SPy+RpSi5RhAXUYzp2o0n4O5Vk0UxyzYBgwUlFjrnK6+H/OhtKpXkzbQzCedH7mfG83bnmNnM316U0XoayzHWNryLWqrfEMNY7n8ZdH78akfakkB8bEd6tsp1QGTzMZN9iHtO5qJz3r1a5DineY1J8icWP6grdbPbLPNH0sJTLGjAIz9ofgFLRcXOkzZqI6KXYRm7n0a1uYXBe1AB3oPiSSjDfvFQXkhoNMrKJxZhKYCrr0uDRt2CzGYBema5xbChY8ThPexHHCRyiDGFNv2XCpqevVHf4zcbsjrseTy4orA5fQk+zXGEIITwrSpFLOtOEckLlGJYQK2OnAc1/sxwtf1hDbbJzcW7K/1YcxD72Py19cix+/q67Bw0rUc20ryYP2w9aXsMNxDeawO2H/rAmrZo9FUZTsMeDKEmRrASzhUds5RhQHu8DyOfFaMQsDC2I4lVUbiH/dfj9O7FgJ+xfNsH/aBCak/Sxb6v1woxe/tf4KJ7HmvQI92dpaT1Gd0fXgUCd4jkFzqQMhYshNjocOPodXtt6dlMz5YINYxLVIQ6NGgGg4Xtr4H1VbkE2NL9mxAArCHln7+c3xLJ+JFA+xdJJxFZP3MYIxy0DSurv5X39Fjl873DD20AEs3LyW2Ja4oz/8s7qm27B6uReVEwQZ94UzkW5d2daCn/7uZ/hduRt5OlmBRxLldqum58UIzIxQnjTxSW/cKV+IPVdH55lJkTFm4ljsE13DHA9cDvqqmOfog47TJJPcM7cfAwABnxz8MdFzdDw+Qtd+seKpwIthtOE4gAeER/BLQSSlCkLKr6PUtZmB1Gr+DELq9hnCG2npvxlciL/j2rg+DgD0NpvjJwUCtZrtr1auxs9GDcJTkqyxwbx23ReSMfMYHoFVkqGTJeErXVeuz2foC3IoXjcAKHWmBoZyuxU/eCOlgPryau3MC7ZH7TF4Mu+3GHrcRuyvcuIV20/AhHkcPOTBD/+9HaShsLXIBjvC4JgofBRjgCSWl8C6KXmwIoa5tu3gGYBngPZ8K0I2Fid1rMTL2+8HE4yBIRCSp66TT3pjmUPY6rgOp3Jr8EebOuunNNSu2mYG7QTPkxRRiZBfJ4EfpIRNiGJR13rUHTsZe786BZN94oKEAdD0xbF4byMpfVy8Rhavz7X4fP1VyX8/fOBZGV+IiKy81L+tDmDeHbrXyCoiyOFz5EVbdWM9lqz+AsduIHs2Eyj0aHvYKtvURvjx69Vcuv6UVZi7fROyb7gODomEwdEskQIARTZtY9umo5YMGPeKiPuKWUrENsNnUeOpQ8ZI4BljJo45K4qQE78bswmk0akHxMGgyEu3PsfWmbNMcyeYIzktEj6mtlVFD6EzMAokz5EFUUR8asNpPLajBOKLfuDdVPaE9CV8VrgKpUgNBtLU2QSWKUi0JJwv/J0o/mcE1wnP6u4j8NakQUZCy0ZzIn05TAiXVRQk07YB4FholZcgZ4k5eflAykhCCHNtA5TuzgB5DnJxxHGFKY9cdZYdmw+163rjtIai0SNEEuyhIc7ktmv+InqyLuXIz+t53Bd4qYzOJ5tWvpnaFszikGfz4Mt5hVg1Mx+fLSjClom5+GpOASIKld8iyO/Be/57ZZ+foKgGJ/vZoiakXtQkaqXcfuhvqrvizU6tggUAO8bKVXGVCyGpwFvCI2UEbCSQCgHFwQCoDKpDgsd1igbc5B61Mva4XvnzVxLpxA31r2FooAGXNomlTUIXvog1a05BXSUhfV+ph+KUZFhe/zna2j/Bho0XISAJYZVO8wKsgOKJEjJ1/D4oDZ2pe3fAGovhVyHtGldcH8IbJKG7kyXieSVdIi8vZ8kSU+fVWOuqkG/lkNXPUiJaYDWMMyfHwqIRZkrUlDJbebuTkk0l/Ym0znnfXnpSgx4yxkwcc4QyHO+2otzKIBZQr5KWrRdXu1Wt6sn8VuEpPPhKK/SKEjsEOa/gGeYHpvp4BujqvN/zvASAPO0wEBALpQZWa7u6AGQ0kCqvkC3RVcmFF2xMAP/hWcltSuNCfzIETsVbOBbkQpanCmShvgQWUY5TomXDJdS2aNAcp0aAAG+PnASYR0lpZ+NZNYOg/yKOQsq7U98wMKnu10z9Ky4dS+ZXSNHQ5ccK++14ZcKdeOyYn8LKykNQz5SWIs/ejZ8vfASnD9fProkqRnKSMROzsBiZcxC5VnpWYHFWB0I2+tA0pkAM9wUdchf6pony5/qvtic1+8vpPLfzPGqdm5/vfQqnrHkP3699UbVuWDct9Q4FsgysehWTyf4qJ3nH4x+Sf/6/ocTdYoSVdm5MNMbPblX/FqQp5UcHfofVay9OGku1zDb4evdg33CygnJv7wE0Nr4GQYgBQ+akGiqmYuvW6+HxrMWuXalxLqsggjHnNqFovFpMTjnHJUI9+eefh2O26kgDmIQlrinGOp3IPvEEAMCS1V/i6V/+GO/cfW1SO6by5+bq9zGCgMHdXRjWoK8v42BZjNTgtAwklAUwr1l2Mn52n2jsj3Y5kBvXl8npg7G1a/Mm3HDGqZg/pALHVg3GY7fdim4Dwoi8IOCrrj6IDMbxnTJmCr301LGS+I82xMYi1F4ga5tyMJR88RkAg3nSg8rq+tL663TMA32Fkhs3QMZsz4Nb8MjasgX5g9TSKSc5N66W1zu6AC9jgrAF3xPiKpcxDns9p6DmA7GII6fwzkg9OUp14QQ48JiBdbhQUBNeLwJZY0IJf7t2lXNPzXxqm69xMoJdgwxdB4BIbjRAApySFcCPYdwovRwvosISwJOjBiHKm0/lNgILiYxCQEuXFxVMJ3oqBFRmN+Pu6c/CIgnP/GH5HpxS/REKHB6cMVzkTygrX0vnny/mFaLClcoKGewiC9oNmrVDk3l2meUj7BtGmdgB/NhK5jv15Mjv5zhWTYLOaxRDWNxhtVf0ym3yjCdSyNaCGIp7OuKGEP1bGEkU4BWjr9S7lcSgmVARSmLkDD2SMZMAC0GWfn1fDd0rJe15TCc8tXrNSdi1+340Nb0JDJ4FXPEf4A55ynqXZzWi0dT9VnUzsYHQ/dyzzwY4Cx6TKO6mA3O2i4aqa/58uOPeF1YQMHXvziRPp+jmm8DY9MN/UsQ6OsD1+pLGkhaKbBbTno++oJgQbrLGw2FuRf28IVk2GfGXYRhT4tmtTU244YzTMGTYcLz8yef43Rtv4cDuXbj08iuT6dk+QiiqNw18m++UMXP1xz04bw05Di5TdITcWq7siIIDMNPJocLKYP42eSy5pLERAIPJNdrl0aMwVxxSCRsiRFE8AGJeNoDg7gX4Ha6TNeX75GZU+/YzZJ+9dbNln3Phxf14LKnRwgtiv0PdojGg9MRI07lp9X8SqEbfCuY1rb8MdZ/qSK8LZLJbpLcQAm/F4S/uoh7a8LU8HVYAD0ajzEQCt/uvQhVq4sfI3/qbB5eo9i9FC14t/wJXVhbB5yWnlR8p5CiKDI7MPwibJ2X41rT3Yk65PDRoOdijUgWV4oQhKd2P1TPzqftpeV4EAC0l2qrEWrAjjFKK4V+xvQa2Va2w7PSozI0nO+VEUVql6kTauNbixAhfgsSTCdpT94UHsHXWSKyM/TOpoKyFkrB2OEZa2fr2OmMLCFrWHx7qQCCQ8kR6vfE6WtULgbzBqt137LhD/EdxXDH4lF+kGlkOuPxtMM482TFDjmtH2aOPwDaosk+hJBqu+/c/MTguxlf64A/hmjs3becWIvK5QRAEBGM88S/A8+iNxajt/f1L8F3K7fRFU76Vw0M3XY8NX63AP37/LApsVjAMg02bNuGSSy5BcXExZpUU4rSpk/DWy+qFqBJfvv8eLFYL7n/ql6gaOQoTpk/Hg7/8FT5+5y18sGUHeEFAW1ht6B0Okg10M+jf7PotQ5mFwcGY9iApAAh6xgNDUtum1IQwMYtDhY1FhY1F4+58QCw3guNrl6PnAwcceQzKPdrW5Xx8iS9wArHtXOEfeJ25mHosI8Qwep8P5458RXWO7ws/RvP6y8X+81aw4PED4TG8g7NxLZ5D9yH5y8pHTRJPGeVH+cDCgseEnV5sH+cmcmqkyNfwLmmh++BCQ/tFegtgdcmvEQnkAQAEirEDAD0N02WfeT6Mmppfq/ZbKvwH7zGnJT9zoP/mtw0twQaCE0+Ih6V43njdkUOf3YOhx/2C2l732T0YotEuxU+Ee7A25zlcUPszbBkn52qMYw9hPVJqxi5rKjQ6kqnHPmEQ7qkqxX37xMy1jnzlQGls0lGWMZBCWa9JDe2l4s+tf8A+vpLYxkJIZmhZCc8qV9+L2CAXEIhicIicvXUG97VuN1qKtcMHSn6PdLsjviZqKbGjrV1MUV8/NQ8LVmu/OzYhioub3sU/yk8ltl/c9F+8UXoSJvTsk3d99o3AmueIx8gw6UJgqxga5Rlg5apFqTYdMml7R9wrdMsagOcBZa2vYYtQcK88pOMqDQManhGzpQ4SOOPLVKq6tUS94Egga/p0aptRBACcsXl/v8/TFxxYOBEujgPDMBjksKGeYDAwDINHf/4UDu3fj8kTJuBnj/8EAPCjH/0IO3fuxHvvvYfs/AJ8sX0nQkF9EnkkHILVZpMVkrQ7xPd50+qVmD52NPXY/pqr3ynPzAyXReXaVaLcyqJrj5z05WKAoZKV0RBbaijIrZ8BuzuVRXDXW3Rm/enryQXNAGAuRfwtgUJ0oG3/EhSgE+OFrbK2/M9ORMgzRLZtErbgQTyCMjShY9fJ1PNGg/rEQyWvVmnMMODBaxgKUlT2I33bCEgk4KY18UrhJhTX6uv/mhqAJbgUL8k+S71UwyAftPIoHgyfTzsrioRA22jEwuRJvmv/Ivjb6IOEEtWowSVuNxZaNqO9SD7pjgiRU7oB4B33fRjONGAGZ8d0xoqxW7aqiA4Opv8rrIiG1wbQ/hm7cq04nVtFDfNIvYijWbW2imWXB9aNHbB/TdfXyWX0jVCSICCQGrC/PEZdjgSQfzefK/VOhW2sbqo3ADy9l16baF73Zny27kq8vVkhJLf0/4C5lJIGUiNFEnblFeGn3l5t2QIZKEVLs48/kbidhu+//MfkvyfsN/5OJVKwbUPJ3KNkf+bNAwDkX3aZqX59EyFInntlZGtyeQncDjvys10oKytDWVkZGhoaMHXqVMyYMQMjh1VjznHH49ilp8iOK7RZkG2R/5YzFx6LjpYWvPSrX6LSwsDb1YVfPyoqz7c3NxO9Mgl09bMUw3fKMwMAw5vV6YauqHxydiok7We75LdpqlPymXPKqpi6QnT7ctJf6wGKJp8b3bhO+B2eZ24htl+H32Nlz/eQu244Tpj5IXZIVs+xMJ1fAAAQ6D9zswF9lpatcp0JtTEj4POme1Ey/lliRo+S+Hyz8AyeZe7QvKZVCCPCGItXR3pTHKdoMA+27A5Fu5jJxcfMxb+NQDouLMTneB7k30+Krq6V6OhYYfpah7+8A1UnqoUF23ecbvpcnq2/wc5RalLnZY1vYIP7dMzPdqokEFfOLsD0T/binU0NOCsnCx8T6ubMGbQez++83HR/zKChnO5dbSm2Ib87gpO5VHisudgGS1RAUVdEM20dEKO1XFt8otahNGkZVWwMxNH1cKUDQxroK1xpenDdYPl7XVPlwsiD+hmQPzz4Bzw+jFzle6yfYqxOugBY9VvVZlmYScYhk395j4es9ZIuuJctw4JNa7FiqrooLwD8+A9P46yf/5HYVvrgg7LPST0ZztgCzDl1Crr+9jdim7WiApFGerHTLABvTyGLbY7u7kCsx4uQxYbDZWRP4gR3FrZ7yeroI10OdEdjaKWo5Tql3hHJvwsViyyOYcAxjIy/c9NNN+Gcc87Bxo0bcdJJJ2H84pMxZXaK4M0yYvgqEOPhi6boFSPGjsNjz/0RTz1wH37zo0fAchwuuuEmFJaUyLw1JARMaPuQ8K3xzPzud79DVVUVHA4HZs+ejbVr+/byLCCws+e0y63FE91WXPhJKpMhVyPfzoy8uiUK/Ch2H7HNiQAW4VNZkUQpSiESK7tr52I85Fk2Wnrmjauv0eyTwOvbs54Dx8kvp+DMMBDQ2Tk1/m81HsAjss/zQJ/IrYL4YjyCBzBO2IZbBX3iX5uCA0SFQe9RX8GCxxjBWOXjzVuu7MMVyL9zLGQ8rTeB7MJ/qLwyAGBnwpjkFfDeikMoyVLrvXDg8ccvD+KLvW248Bg6P6rA0bdwohF059KtjMSrMIGtBQAE7Cx2jHVjy0Qxm+0N+4+I4aUUxBMMYVIhphgD+JycITd4e74VK2fmI0zhuNCygpJX1xhPep3Gnt/r6/+F+2uex4cbrtXfOQEaEVW2feB1U34/bihYAGvWXAiUT05uL7rpRvzoj2otoIqfi56oPF8PpitKEADAyBVfgnXY8dCfJMVnE8aMUWYrRdyUzcnBkBe1lboZhkF+JAQHx6r+7L4eOBkG+bEIsX1CThZcHEdsc3AsCmwWVGXZUOW0q9qyLKzMOMmWknrj/xcEAdEo+V1YunQpDh06hDvvvBONjY244fRlePqH9yfbhzps4BgG2QRRvlPOuwCf7KtBQ0MDvqg5jJvu/yG62ttRWVWtea/6i2+FMfPqq6/irrvuwiOPPIKNGzdi8uTJWLJkCVpb9SvCKjGY4MJ2ELxb4zuG4ob3uqlho8q4ATSiybhQXlvRJFQI2mEWWi2hrnemif8QLMiGIn1NYwT01s2htukdCwB8WD3hTcFG2efEGZoJqdEWIYLhoOupFAryCfM5XI3fCteiGjX4IR7FXIXmT8hbpjqH7ndMMxZxmwEAUwR1uujMuMDgEIcNPjOudw107jHmfo/46YRbo2iZJj7PHn8Ebrs6ndIRfz5X7GtHjpOebikV5zuSkArR8YzoTVJC1J8hT8yJMNSX9juT2zZNysWaGfloLZJ79ki8mC0Tc03VY1LCbO0pEuxCBLfXvYxJPgPPX8Gw+EFquQaUT0ZDg8Rg1SHh9uiET73erYRt27B794MIh0Vv6lml+Wg8bgqG3rEWuC7FibGPGKFaKBXedCNyT0vxgyyEidlSXAyuoABl7am5IpF2XXjllZr9TR1A/lGq/vF32KqqdA+vaG9Bvtdj7FoSuAyo9zIMg3yrej8XxyLm8yF86BD4SAQMw6DcboWdZVBsExcDHR0daG1tRTAYhM1mQyyWmgh5ngfP8zjzzDPx8ssv494nfoY3/pLKIjRi1paWlsKZnY0P3nwdNocDc4473sBRfce3wph5+umncd111+Gqq67CuHHj8Nxzz8HpdOLFF18k7h8KheD1emV/Uty/Q+7mJTHlT8ixoMQbo4aNrvrEi/duuwJZYWOrldtX7sC2CTfAqUH65GMWFECdbTVB2IKYf6nG2eUvW+3H91P20z+2bZvcyxFuUadD2xHGX4QLMENYg3MlBSt7m8cRzq7tOiyKq736msehdevZcCCIfGjIs/PyFzfQPgwQ6I+xv61v9ZS0cFX0p1g+tRp3Q61jchLew93CT7F82kisXW+uxAINHbvjv73ORCfEyB6Lrv3Hgo8aTwNnGdGgifGEQTKbLMYnxW8GPYF7Zqgl5GnortU2RhtWXafZLkVzaSoE5VcYFY2lKePjBHYjBAB7h7nQWmgDH7+3F3FynlSUTXmCpOGtrlwLmsr6lnGlNWIkwkxKzR4A6CiwkXkzIxb3qR/iBeO9yZfwR0adLGrbXCxPVW8cHuflFY9FLKbWA2lqStV6CgbVoZf6enkGFc9HsG79mWho/Cd273lYvrPDTfWIJJB35pmyz1ZKOnT2okUyD1Oi7lL2okVoa/sI69afjWgx/VfhcvNU22IssLHjfnz62eVYee018DvppHWO51Hs6cSoulQmZ57PS93fLGwsizyFQTPYYYO3uRntFguCTaJXv8RuxZjsLFhZBl6vF+GwuDDx+/2oqqrCmjVrUFtbi/b2djzwwANYvnw5tm/fjh07dmDlh++jepQxXt4rf3wOtdu2YO/evXjl+T/gyXvvxm2P/AjuvLy0fWcSvvHGTDgcxoYNG3DiianVKcuyOPHEE7FqFblK8xNPPIHc3Nzk3+DB8lRBTvHcKj8DgE3HBckAcESMkR2HCLVwHy5PHUhALOQEAwEjCfV/tjOTwbDy7I+JwubUsQrOTLBzGPb++1fwvUOOm8ug+O4du05F7cepIoa0DCALorgTP8NZeAMtmy6Mfwf16o5UhVuKyoTQnMAgaqRYpGLpGugcRt219uMHUPeZPJ073EMvIdG+8xRqmxQWxFDau5woFsiCxzRswIEtZ0GIGZfDD3TSyYik+0oEJdzobx0D1mLcg2hlRYM7z+FRtbly9MWvnOPoVcJJqN9Az+IDxDBa/Vc3mzonABxQiNDtGi2G47433YE/2Z5Cc4kdhwdlYdt4N744phARjsHj1hdhl3hH18xIebukInebJpkTYZRC641oLBMNLlpIqaZKkQV23afAMp1QLI3cCwACwS09eBaw8B4gR+4F3eV5GbhpJXDdp9i5817VYYcP/zmpJxONEp4TRSirviFl3JgiEAM4cc1XKiKxNSp/xp2zRckJRrFf8jXhOGzddiO83i3oulxuCAlFRejsFEOlzllqouOWpePg92+CIHyNwz4fvlxoLNsygQKv/qLADJRaMhaGQcDpBBgGHouaSuDzpYxRhmFwzz33gOM4jBs3DsXFxbBYLHjiiSdw4oknYuHChci2WvHS3/+OHAsHB8cgR+I1Uk6V2zesx5Wnn4qJEyfizZdexIPP/BoX32j+/TWLb7wx097ejlgshtLSUtn20tJSNDeTazbcf//96O7uTv4dPqytzDqtKz0FshKY0CZ3sd+Kp1MfLECpoB7saz9+UJP7IgUTY7AAogvWLXgQDahd6XzEifrgDN1zkbI+gp2p2KaT9eieo+vAseI1o+JKNcF7AVJk4VhEvor9kXAflgj/TQrmMWxMpXdDQtO6q2Sf23ecJvsc6k4R6cTvIf9+UqVjJcLect3rJ7B7z4Oa7T7fLs12JQ5/cRd8jROp7Twbhp5zl2EoXjCDz1UCte3iQHftBDXpcVDVAVhYczXI9BC1GFH9NB9/IXGCAOBii+h9aS9IhY14jkFj3POyzS7yzGKMXGWYl3ShP3V82oroRPSEvg7Lk3+zXmUIq3I6YNPm4WDJ4/S2bInBkhc3qMeIoRtiXZ7S8YDNic4ucvYlzyfeff37Qwo7aSH/8lRW0fwt68QUbwBFt4rG2nBJ8cjBzQ2ofCbFsZGOcwnPjNTIEZzy7/rhiSfg17/+NR599FGsW6cuw9JcITf0uglehxjHIVhWBl5ynVEuB4b2emFRGF5myiCQ4FSQmbVOR/pdR40ahVWrVsHv9yMcDuPmm2/G559/jgMHDqCjowNvv/0Wpo8ehWFOO0Y5HbJSCS7FtX/yhxewrq4BoVAIy9esw6kXkhcrZsT4jOAbb8z0BXa7HW63W/ZHw0+3BHByk75aoxlM2yYfrEoDqcGaj9gxEZtl7ScL/0GktxjdtccYOr/Vb8Ex+Ar3C4/iZ7ijn71NPVEtXGoyTKjt1u67QP8Uinfjp7gn+e+E92L/f36Ojt2plPcR2IfL8SKc8ewShuENEXSVKeh8RL76btt6Djr3nojajx4ACQIhJOWtSxh9A6/GSULXgYXgI05i3wAgYvWio2Q1enN1Vq8UYybUXWGqP/tbxefVYVF7Hp1OP06p/khV+uCbBNWEr0RcQbe1RG7s7K8WnyUbIy5uogotGK/bKjNo+ort47Q9bTxnRcRCGZoV1+/2bkF3z1ZE9GbDSynFYM/+Q+rft6wF7twJFIvhhOZmevkUOgTF/1NoanpdNpGmDB9jKHtA/k5b4x734lvFDMLzPvkvLn7/bVz63r/x258/Aku+xKsmuT0JzgyV9AzIcjKXL1+O/cNT4faoYvLOziZ7YL1uNwLhEHzZKWPTHo3A3qHeX+uJJXFiSCixWeDgGIzPzkoaekYQCMizpdratPluStVi0l1MRDbsGhlM1VnaWkz/nET3upPwjTdmioqKwHEcWlrkAlYtLS0oK1OTQc3ipOao5hQ2XEN5M8bK3Xcj7CyOz7HAovix2ySps4HOYarQy5xtonpmy6YL0bDyet0+e2OlYABMwDbkQN/tbxSHLCkPVd1n92D/u08iYEi/RH6PKpCKlyc4M0LUgc699Ng+w/bfoIwiBj7iROvmCxDsIjPne1vUvJ7mjfSaTumCoDELpjxh5H2CWaIH0hO0a56HZsyEvZXo3GOcV1Eg8Lh07GvU9mp3HU6pphc97QtiMXpWncBG4G8bZfhcYZvOxL6dMrEb8Lb0UCp9pxNdN72OrVPJBqh05OAZYP36s7F+57X4cl4hDldocXgo361AMmFYHUCu6Nnk+RB27lIrbnu69WokCRAEAWvWLiO2trWJIoCCIKCtTV3I0wyU4SNbNIrr3n4F17zzGtx+eQq7fWTq+Uncif01kjR1CvN64qQPsWDh37Dr2OGorxTvzcHT5d7gqdPeo/SPh8vVBTYrZfjT0riVysGDHKkFcY4BIjAAlDtsGO3KAsfHENxlzjOsBV5iGP30pz9Fdna27G9iSSHmVpTglnPOTO7HGXiX9PYZ7XLg2XHaWkBSfOONGZvNhunTp+OTT1Iy/jzP45NPPsHcPspQT9JR6pVigsYq74uFv5J9Hp/FIYdjMEhhAPkaU5owEICQolxCi12MtwoxO3rqKUI0UqQzQ1Jyrp02yX0RLIj61cJedZ/fha598lRtrf5INWn4CH2wDXnFwTvcQ1fkTGDfW0/D1zgJhz6R10RqNDDPKCd1f+so8GFx5aSVrLHnDbUGhxk0rLpRo1WQ/Y8Gnrdgz5vPYvdrZD0NapgJQNCjlpin4c7Zz+C4wV9R20flHUB1Llnqv69oaqQbK968XSoPnDboxQQS28MUBV4ppGTiBA4NNqme3Qds3noNYjxZT0Y658YUfvq9IzTCTQSFXn7uTaitfRZerzqlef8Bspr0oUPkZy+Bnbt+gM8+HwfawxwMNUIQBHz6Wf+I+WUd5rLlBIIkxytvvaV5DMeFkZcnLqKnz3gX+0aNxPCPP8ZuQkgpN1etFm2394JhBNjtEs8HZZBRhvutLAM+FEKktRUMxcsS8/kQaWqCoGjne7W1iEhhpvb2dgiCIDNcEmhubkYoJHrRbrzxRnzwwQeyv3UbN+LVFavw8G+eVR1r70csycmxpnzl3wrRvLvuugtXXHEFZsyYgVmzZuGZZ55Bb28vrrrqKv2DCRjWy+Pllb3Y1xkBNFx4Vo07aQGoahXKw2LBPFnrFwy5pEECp7Wuxn9KUhkejOLhiwbygXy1eqke9lliGBkVv68Q48BwMQQ9Kcu3jcSEVsDfOhb+1rHIHymVEqffKKkXStAQrfMeEr9vuKcUthx5yr0ySyoWzkH9VwoVU9BtgRgXQJQLwB4uAAQLeltHw1WyRzxGlrGjoScU03aJRoM5sDjoXjJf42SEvKWwu8kS+YCWqJ/km1F0gUSFT808GY02ObKd2hoxDmsIE4vMrfz8fjecTnUGR8hbBm/+LvgOTcKgwTupx0c546UfQjYGnaoyCyI6860o7IogZoCkQCq50FZkB0x6Q7u6ypCfT+b3mYU3J/X7xwwYZL4r/46NK2ai0nkMlHmJjSOG4sDBX+DAQZFAvHDBJlitYgjs8GFypqggRNHTQ9dS6uj4XLM/DBh0E7w7fj9dvkGKZ576EZoLizG6jq5UTb6w3JgZ/KcXIHyUKmuQIHDYR4+Gc/p0OLPaMX36u7JjequqYBtUiUAggPx8eZjV5ZJnYPIKrxHPsmB5Hryf8hwTfsrQ/v2AICArHAbcal5kuLZW/IfFAmsxPbEBEA2YRHjI5/OB48Jg2RgiEQcABuFwGH6/H93dKWIyw8TioW8GHR0dqKioQEFBAaqr5V7vvIIChMNkg6vQakFjsG8cOxfHmTJmvvGeGQC44IIL8Itf/AIPP/wwpkyZgs2bN+P9999XkYLNYEwPj9N0YpFjHPTbMy5L3uaWfBzXkTJzsv1yLxBpSrHz8h97ccdW/Fm4KPl56Sb5JCBV7T28Qj2pk/BxVhj/caVewL1v/Qr73n4afFgcsL0aq3p90B85Bjx66qfo7peQ4094aKQg6ddIITAxbC7eAoudbCh0Fq+Dt2A7wjZxwAl2pF7Glk3amTRG0VOvU8dF4HRLDrRtPYe4PcaluAUC4Qnqce9De9kKNDSqzx/oqFZtOxro7CBXLN9fJxYe5HXEG7uK1xu+1o6xbuweSfZSdBCKO5qFkaKPUjQ3aYfImppGGj4XL1npKrO1lIixwJqDtyMS6URt97uq9t6gvHBYff1fdK8v8FGsXWdecToFBlFCWjcACIQq9YIg4HD939DQINaEOvmaK7BkzQo4Jk9S7SvFkJcUldUVIY3sefOQX5BaEFoHV6L0hz9E1Sv/RNnDD6GiQq2Zw8azgtzuVkyYKE/hF8TOAkJ8WaG4XkxHbVj1VvN80ovDezyyStaqXQkGUpSjv0+BQABZWT2w2/3guNRcJZUwYdkIXC4PspwGsq54HqV2+fUS357VCCXJ1hOJ7yrZ1NHaghhF1I+Eb4UxAwC33norDh06hFAohDVr1mD2bP3Ml/4iS8NFVm2XP5zSH80iMHjgtU5c/tFe3PC+wh1KiM2WtXGqfaTiedm9yoycAoz8qBK7//UH9DZpv9QJNHA8YtJsjJg9qRzbyMWwIr8O47jm5ISfLjAQVIUutdDbpM7oifRqh578zgZUct2w5WgXdPPmiSv/9p2nom3bmTj4/qMI96R4V73N4w33UwUjxiCF4Jvg8UQDBcRwVsTuSf475FAbbEGnmB1XVz9WlTXWsPIm3W7FQk54Ds7T3a8/CATIpNeUEZNe8rU0C0mKw4Oy4HewKnJvsj8GuqFVJJOEHl8Bdu8m39/9+2bh4AHjBQ0T5QW82RZiGEyKvaecp9keDMuzKlvb6LXjEuCFfmayaRSkFBRp4p7uDfj0s1HYu/dR7N7zQ8RiIeSddSZGb9mMqn8qi23I4ZojenpjsRC6utbCMlQdZp0wIeVdDkQOo+CyS8FmZaGrq4v4NCY8G4OHqMNy1dUbwXR3Q4hEEIxPzBYTcghSY8vGMrDs2ytrHuKQG+EjXSlPMd+j9hTyCuNHCKfmE2mYiWFixO1Wm6jFxrH6lAyPx4Myuw0lNnOBHhvLosIhelCFUBARXkCXZBh97rnn8Na//234fN8aY+ZI4F2F+m651fjtyZOYmSwjatdMOdABd1BpWcpfk2c33KyawJXxU4bwav2n61FNsTgaNtnUlm7I5sEsWx1mWQ+ju2BbPA04PWAgwNc4Jfm5+5DaCO3af2zy3/620Tj0aYp46KnRz/DyZxvjbwhsDBFLD4SYHR27liHsFQl9IXsbetx7EItkYc8bdLG32o9+SG3T4qukOkCeKaXcJCFmR82HqbRvGd8KQNguGptd+xcBALobpM8Og33LH5OfO56KzmikZwsCm6xfNVDo7i5J6hHJri359+rVZM9UMKiTemwSgSwONUPJXo11U/N0j1dyVfQQCuZQPVO8wILnjQsaMpw4iW1TVDtXwu9g0ejVrird3iUvK2KkAKog9JOoL/DE8Uw8t3zi3LDhfEjX6kLckGLtdhX5l4YdO+/Exk0XIWBCBub1118nyhlo8VU5LgYmEADz+Rdo7/LAE40hFOIRDgsIhwV47HaEeJ76l9feCiEcghAOobKzDeFYTNbORMLIE6IQwiGwkTDYSAQBAH6GRYjnEQwGk3+9oRC6HQ5Eo9HkX+fBg8n2cDic7FckEpPtJ/5FIPCh5D6J7Ynj1fuLbfngk98h2NuLpqYm9Pb2JrdJ/0og9tkZjYD3euBpb8eHQR6BfixqvhWcmSOF3yGIU2HcDc1CfNXKrQwmS0SuEq9Z2JaDKCcfNP0tYwFJJKV+/2NQRgYDbaOAsRKGfBoJvzHCsyJwckVkng2D5fvmjuejVrCWCFghBp7hMAJ7ASEV/gh2ViN36BrZMS0b5aGeQHvK7S7VjaHChI6Kp2gTiptTIxvPhuHNF/kfXNQFp1896YS6Rf2ZYFdVv/pAq+Ssup5nKPa/+yTcQ9ag6+ACoCBVJyyU1QZ0j0Xr5vPR0zAFhzm5pyYRNlRCKxso7CvFQNfdCYVc8Bw+BqVTX5E3SAy8SNiJujVXYMhsMdyxaeNSWK0hBHUqu0eDOahfezmqFhpTHd473AW/kzz0+eLZSgcoxg5gIFuKgBiFD+XrEQ1ZGqdICYEBMPE8BB3ahgrNWNM9v4RbQQKJ72IGe/c9huHD7iG2SQ0lcto2/RllHA4IQXEcG/KXVLgskTHl7nwWI5k5KNqyiXi8FA0NDZg+Q10qJzdvBYC7qPRyAYDlnXcQBVB3ylI4ouGk4RYK+eDpJReMBIAI14WOHjH8xno9gCK8YrVaIQgCIjEeFo7FwVgM3XHhO0cwCIckM6u3pweRmNwwtIfCSFDXPR4PHA6RJByJ2FWZhCJpORW6CgbFfXvjxGKPx6Pqf6KtNSguhLujEdjiWjo9DvWzaHfY4IEYXWoMhPBhkMdr4dRzd3Kt6EX/nyMApwtdUR45FB5nb0xAj8mSKqflWfFhdwSjVCGn+Dldav5H577jgamSfQnOsUS4gxOiiDEWVHb2P225ytqMEZwfgDo+H1WkRQuMvmtx75u/Rv6oj9BdM1+2vf6r72HIoqfxJO7El8JxOBVvoQES8SqFN6mnfiq0HIRSj4ePDSCbT282SUdxyrDiOfFFjIWd4Gypl7nu85TaaSzkBGdXx6hDHvLKWwYThXei/kJ07j4F3XlkUqzAW9HTPgx8sbxCOM/JXds8E4HPfQAMz6kIoAk0rr4OuVUrKa3pAc9biARwpYEXDabCUT5fkaFzb1xxJUJ2D6oM9oVmyCSwc1S2ZqmCnhy5J8XrLYTb3UHZWxu98YrvdYcmYcxYegZZAjwfQuzM3wBfTNDcz6ROovQK0FY96T8OHCRnSnV3d6GwUPTCRSLm5P6rX/8XPG+8icLrr5PpyyRgQRQfDS/BwRvFa7/11lvIUUQ+/f46OJ1DkJvXBCeh7tjQoVtFXg/l5oZtNtjDYVjefhufZLswf8EHSEzHW7csxolvfU7tf1NJGX54szjO/OPPf0bkkJzPlH/TTciTpIR3vPgilku4MjdcdBGshaJh/O7LL6M2bnCwbAQMIyAWs+HWuLjgb3/7W0yf8TYAoO7QBLS1yUcGiyWIyVNSafMb1oslbq699lo4HA789rfqUHji3OuaOvFFpxcjvvwE1jgH6pWZ6oSXr8aKXD5BELBs9W4EJHOAi2MxoaMRPgCOSAgjhQiM5K59p8JMWlNJhNV/+zdxNdjM1cq2zcm2IE8Rf9ccCgQyUQqErX/AFXja8yC1PpQZjLPXYZSlHT02daZKvlMepgk41VoI+yxyA4ePZqFj5+kq9WF/61js/tdzqEQDLsLLyNEpihn2kcMb3bVzEAtnobtW5BrUWUN4mRBtEBQlBXjGZExfNjDF1YpD8gtJq1IfXnGH6hSxiB1d+xch2EVOf04o+5LUhyP+PABA2XA3TrpRTuCNcn6EHfSSCDxBidfvkg+CvTk1CGW1IuiilxiI+gsRC2uHcjr2nKTZTkPdljOwJh4+ImWl9vrk96SB8aB+yxnYv/JKw9cISThF6YDZmku7d5mTsk+gUZKO7vEY18zasfMualviFpPmW+mm1kKyp0gQeCIR90jgP/95K9UPmFNlt48YgdIffJ9oyCTO6Bg8CEP//CKq//0murpeUu2x/L3n0NPTg+JiethaEGLUzLS6SeJv2FJWCoejDTzfBJ5vBM83ore3B2xTU/Iv1NWFPbm5iHR0gG1qwuAtmzDts49x0ZBycO3tsn3ZpiZ0PfwwHA5H8s/769/A5/Ml/yKff55s8/f2IhZrwMRJL2DylBcwafKf0NvbnWz3+XzJfg0a/CFKyz6Rnau3tyfZzvONye3vvfde8njl34YNG+BwOHB5dQV+P3EYQj3eZNuyg9tQzzOyP4fDAZvNBqvVinMr1IuWRLmFiu4OLFz5ofaPH8d3ypjpZugejiiAUqYHn1m3I0xIug4ijA3Wg1hvPYCIpN1NSPHUy/qcv4PubpQiC0HkCukTxQOAdmsU/3ZqK28KBAG77Vk9CBEMIfIJUuacv03uCYqFFGEQyr1qWnsN9r39S8RCOQjZ25BVuAbjrOqK456CLbLPvpyDqn2U6M2u1dkjNezXr5DXteGjatde556TAcGCui/Ik0xC46Vrn7pqbCLDqcG6Bn9/63mE7CnjxUwGTwIheweCnSmV5KgltXrbuoUunNd9cD61DQB6DuuTVPmI+t501E1HOF47rFfx26z8+gJi+KWm2w1fe/oLhA4UQqFs1NePNX3cwQOpciORiHGPY0J4joTWYnp4+BCTKtexbTxNhZhPclOONFguRUBetUr9rhDLKxhEwkBzzZ0LZngxhgxVk3ibGpvx3HPPafLfmprqqG32yw/Bl+3CF4sWYdr0/8obFePcV/PnY+uUyVg575hk852vvIi7q8s0w3xGcLCrC6PHfC0jINvtfrQ0qMdPAKio2Au7Xb+kyJ49e6htH330UVJJ+OOP5YKaZUGy7s2zzz6LX/ziF3Aqvm+BtW8Bo++UMROipMc1sp342L4GS+27cYBrwXLbxmSbAAFbuFrUcK2SbdootLAghdXLrAxyOWDG/qC6kQYT7+87zjCaOfWLKPVejODasd+mvfIiXXK+ayu8BdsRsRpz/yZ4Jp6DC2Tbexrkk6JmVe24UdQTl/Efa21UrdiiNrmx12mVG4qbCdov/mxxQIpy8n0DrvjLLlnW+pom0/sXhydRmyqcTZzQExAURM9oMAetm88X++0RHanefLrWSvI88V8oRPLasDxVr6bbQ689peybEtJ6XSTsfu0PYK1yIzkWksfKAy65x4/GIzGDQ4eMZfMNNGoOkuugrfyaXg6EVsRVfryaNK2F2sHiPQ/b1EN7y6L5iNz4GXDlco0+CeD59JV3iZqo1l5ZuRsdnWKozQhnhucj2LnrB1i37iwEAmrdrX37UqFTqbdJ4MkJDlXVm1BQuAq5uWq+TAIffng3tc1iiSBod8BqVS9WlTybziIxJNRaWopmlcQIg5aSEixfuhTNZcbkRzpfkqfVWyzq+/f755/HgQNkPR9ShlYCpO9DQmureN+U14jFyF629vZ2BINB5KyUp7m/NLFvchLfKWNmj3UHeqE2JJbbNsHHplaw7WxqAmxgO7HOegBfW1NW6V8dX+hea5AiEyqXA2a7LFiUY0VOUMDVH3XjpuUeA702ZqXzbBjIasI/s9V8Dp879XAN4uS6AW0syZgQFJ9Sn40VBQQOfXI/uK/nJ8XwUidTPHImFyFRq/b1XVYv/K46xFjxd67MoxP+ugvIhe5a41k3HbtOVjcSeC/SMJSyECYgz0iq++IOdO1fhD1v/A7733kasZAbvS5zarohRxt6XYcQcJGFE5vXX45wTzGa1l4JXqJRo0WkECCgaf1lxDaxTpdGOQbPIJCGEm/9DPSdWKz/YGzedDLqDukbm0cL+/bN7rfBFosZNwYAgI//DN056tWtL/YZvtx5HvghWirjPJpb3jZ1TS1s3HCqqf03b75Co1U+Vn32+Rg0Nb0Ob89WbNp8uWrvusOp55lhAL8/TpKlpIc7nV5UVW1BVhbdG55fQPZuJNCb7ZJptyRgd/RS34QvjluUqmjF8wDD4PPjj0NPrhtfLFqE2qH6kv6Rw4cR86U8IOrsRfHz3/6mLh4LAOXlKVkLpWEzZ+7rutcHRO8MCe3t6kWXVJzPV3sQmycORtOiyWg+bopYW6oP+E4ZM11cF760mlMu9TEmvChSKMbibEU6Z2VnDEU9RmLTxmb7loItmGs9hBNs+1Q8koQOiRSvukKo42J4x6VepbQpvDs+l6TquEHjg49mYUfDFQYOSL10Xzv03dtr7fr79ObUwlO4WXc/2UQvga9pMva++Wu0bSOnCisRY4Pw5RxAjAsQywYEO1MEO3/LeLRsvERGhvXnmDNmenL3ah4T7inHwfd+iu7aearvSOL1dOdth6dwE6KUrKFQT5GmR+7A+nPRUbwGzdvkE5enfjJ8uXLtn5qDU5Eu9PTI+VadneYKag40mk2I4ZHQ0SFm8tXWGjfYEuTmCMEzk0Ak4qG2dXu3YM+ehwxfr6ZG+/cMhbLR2kyjnpuDFpcnENB/h9asFZ/Pr1aQK34bQU6Odqh99dy54Hn1va+q2qxSBZYiIbK3e9x4hPbKNWbWzJUvCGO+XkQscmOVZxhAUok7ka3UF1RW0sNJa9euTf6bUSSK1NeLi6vOTvU9en6InMskLU8EAJzA9zu89p0yZgCgnTHmMusvlD9LlUnVUF9cwr9r/3HJbSdeSY/LWyzi9yrnetBcuIW6HwBYEEOdlcerOWF0EkoYKIeMYE5t8t8kBVqzCEsnofgK4j1XJ0K0lEcJh6eFEEYjgefC6Mwne14MHR81vjrozt+BgKsBnvytpjKW+gyDas2krLT9n98u+3y4bjzCjk5ErT7wHNlw3900VNM47HXXgudC2NeVJ9tez3hU+0YNeyrMP2ft7UMo2w1kmg0INMpjGHhOmptFY+hwnclQGq1Cdhxbtl5Hbdu06VJqGwn1hyckCd400KrBm4V07InFtHl/vl51hXmeF5/vNWvWqtrShUmTPsDIUatV21k2qlIFlmLneHUBXBoOXXYZ3jxXfs/fOPccdDY2ItpFFj2dOUvqbev7GL58uRiizMtrwvwF/8CgQdtl7c3NzURu04a/yRWZIxH6onTnTv1QOwnfOWOmTYuj0UcIEBBTnFf62NoYoMiif6tZSWiq/qvvoeaDR9Bdk1IOHT2nHKPnqLMeDtjkoReLVZs0fIot5Z0qYdT7WlitgYJSQM5ALujfssXB5OD7KWG3RCz5sCVGFdOSQrpHIItcgTaBWJqzXEg49Nk9iFnFVRBvCVEHbjNGYI97b7+MxlicCxQjGCc8gL3/fib5ubVVUjWZ8BvuffM3iBJIz1IwhH/R4O/N1d0HAHxutZqzdIzct1odEqMJA+7bqy+8eKQR0eBWJSAQVviGMOJEzeaenu2a7WYRDjuxbSu93lxUo8AsCd3dm4nbe30pj8HBeD0p4vWiPVizhhAijoMhhtbTg9y8VhQUqMclqzWIg8OGwZuT8H4KyMryIjGe7pigTrUvLd2PktIURaDjRdEgCMUrYtvtPnBxKQae4/Dhj3+CjYuNZB2aH1uUpOiRo1YBAKqHycP4u3frCy8CwC6Nqt7vvPOOyd6J+M4ZM5zJGkRGJthPrNvwZ/tn8INsBIw06JX5q1S/hLcg1D0IygmiepI6jW2d05y3qYANoJDxoZDpxSl29cPnZkP4l0ucCBPckwR63QeJE+0/skPoNSpuIU1Pjx/jZxymtR+VIYyBQsTajciEGmKoJaDMulEYM/v/838QEENX4Qb0uOXuY4DMQQo6mxGxefrcX1+ORtE+RgAfcaFh1XWo270I/nhqOECuNW3Eg0BC27YziNu93lI0rr4Wu1Zco3m8QJBRb2lJhSuaCRxOWv/1jDE9eL3G9G6kCIX0PHvmRBb7kcjTb9TUTNHdR8v7cvgQ/XjS91q/gezpiUZT70pbG5mfAQChEF2V5FDdC5g9+01q+0DB4fBj57yReG/ZKQDE8gczZr6NBQtfxqDBauPSYglh1OhVGD16pejVAdD6s59JzteDWbP/jWPmpUQoo1YL1s3S4kOJKC9Xe630oMx2YiXvp1GC8KnF4kJmCUHsbdWqVUmicF+z1r5zxoyLiaCb0a7AOzxmXPMBAGq5NoAB9nIpbsqIuAGTzQIjFHViluWSU8+aLNo/4gsrDgKRmOpHc3Pqh6krvvqgrfDHcG0oYelk2sPWGAQmhs4StUuWNAF3cAJCfVDqEgB05+3AMdZDho2ZkL0j7TWkUv1RfwdP4RZ42g/DU7gZEWX1ZsVkHw3koad+GnxNE0SvRqAAIUcHYlY/gk61PkVX4UbVNvG0fc8o4ZNhOfV3iVhF4l3P4VloqZ2lvCqhI6mnLUQRsZNOZGu+vgCHPrsHHbvoxE9v3Wx0B83VNwK0SzLQ2sNhc14BEvpi0DVKin7m2dRZZDKP2DcY69edgfrD6nppAFB3iLw9gaamEbBEshHrpzGZvJ6kkjfDqsfQxCSoxb3Yv/8JU9fsbTafck9DQr9GAGQV4qur5d4NnmEUJGIBnQVyPa/cvNRYMniwSNjtdueirUS7jh0AlFGMGdFTRIYrOzXe2mx+2GypRe7MWan6SV2UMBcA/GrsEPx5QhXuy1c/Dxs3bsSaNWsIRxnHd86YAYCPGO0VfVAyJh5myaJlIYjuPenkt956AIdYcVVgjxN+cwiiMxbCy8YAuAI2TM3iqD/Kr/67C/Z/78dit/xFnmmtVe1bbxGNmZiFTARjGO1ABgcefic5W4aWFdNjQHhQCUEAwo4OVHOdYA0IZU2w18KbvwPdBfRUQiOgVQlvL1shI1DzCqMilKUs9Kj8LRk0rLwJ9StuBx9NTKQa94Uy7gp9l3BF1OaNX1V9DhlxWNlM6Asv2Uc5qYd9xWirm4yQRDsoEslCQKc6uAABEbt5Y1TPqFDKsgP61biNgcHOneaE8aSVsqdPFdO2N21aipaWYdi7Zy5qCcTZA/vJ6d2JPhjFmrWn6e9kEIkCoZGImusU4yWLNMLzevDADOR2pi91vrs7pbvk96v1pFpaxPAEw6RPwbhl8wVoPZieosbFxbUAgLrTCjT32zdyZDJ8BACDBu3ExycthgBg7/wF8OTmyhZRVdWbAaTSvekQUFJyENnZ5Hdv3Hh6iYxx475M/luZ0cVxqXF7yxY6X7O3owOFNXvx6t/+SmxvbGxEbW0tQiFtPhQN30ljxmNrg4+Qop3Al0wYtfGJtZYjuywT3p1ORu6l+Mi2FdH4scWUyrwkTHdyuA4ODLGzqCJkIggQcI71EPZwjXCYKHSXYyEbCCO4DgxmPdTjOPBE8TxArTzrdx3GybZd+CLLD54xVqSydcu5CHWXo21XKsbLElLClV6gPLaP2WUK/DWH/sJIU56V6c/dQgT+NjG0VP/1TTAyyYTtBsUGJYhSjFCjCGQ1wlOkXYdGGUIN29WS/NLQmtKYOLj8ceyulU9WifBQxELnbQWzyAqqejhUOxmBQDYOHpxGbG9trVYZNB3tZFVmUxCAjnb99Fhp9etoNDX5u/JEo9bXU4S9e+ahpWUEUWOmsXEM9dzS8+nB5+sbgVILIaXYJYCmuPeJi7iIb4EgsGAFujFpRoPGCFLKyOmb1sLeSvR6DNSHM4CEEWFdqn7+LZZU+kPDoEqMHJXSyBlaJSYyBB0OxNrb8cHSk1FaJg8ll5bph45KSg9i9Bh6JpfT6YUrW78sh82mjgQUFelnk7300ktJAjEJ27dvx0svvaR7Hhq+k8YMALziEH/UBlY90TgQxaXoxX6WPuge4kSPjZL4CwCv20U2+zSn8RVCpcSAIVFsqvLbAa4VKwymltfHs36G2+mDSSVHdyuyELDXSjaEfG75i9ObU4My1oelrk3oKF1NLClwSGFUde5ZgpoPHkMsnFIiVQ6IIUc7uorIYZj+IqDx5EsNKHVGEIO6z76P3a89D1+DelIlffdQVsog9uUcMGSoBLIPyz4PGULO1KHBCJ8opkjbjti7NffhJffCe3g6tAw5LUPKl2s+Zg8A4bAL69edhYb68cR2QeCwfXtKOba2Zgpqa6cAAHbvnkc8xghqasjGkxINDePw9VcX4asVF0N6b4oKtV3/2dmJUhLy+9njTWX9HdhP50K0t5l7NtKBjo5BSQ2dgo7pIHkf9bKYRBJ0+rP/ujzqbCL9ftCh9T2IWlTaZyNutdkCaC4X6Q08yxJrfa1YmBIgVQr7jRq1Gmx8kVlVRR4zyw0YPNOm0Y2NBKqq1N6XsRLPTQLZ2R0YNHhbkjzs92vTO6RwOLwYN/5T5LjpAoZKfGeNmQS2cWp56krOi3FcMz637aAe1xL3apAeTak2jREnd75e/QMAHMVLQsN2WwzvOsNw9SH0AwAsI6CbcmyiICNADmWQhO1eI+jZKJFra5OFdYJZ9HpC/YWVULIiBcnvQQxtpLYpw1A9OhN1wNWArqL+VR5OF8IO+YBJDuOktjVJ9ELat5MJvn3FggUL9HcyAsl3aGoamfSAtPWRo8LzrErPRnt/i8rrUjY0D0XNdGPqzjvvVG07dGiSTDAvpFBTlmLXrmMN96+vCCuvH3/t7f5SsBxDsUm0x7Xduxfg8GGyYUpDV9dqBIPaWYwN9X83fL41a86meoi2bxMN40ALnTfTvtNcSC8RalJi8JBt6CwRCbJKfkwCXZTtCbBxnuTgIZR5qx+haxHGjx8xYjWmTluO6urNGDp0s+krjRm7AoWFDZgiKXiph++8MUNzhs+yHqa0yKEUqJPCygDTXPrmzEKFWqdyCLBAbTSMd7AoszD0VT4D7LJFcZAzbtlKIYZ89I2sXiL3RP3QF7C9mGzfn0wbJqHEWYuO0pR71ckODMkXAG4bSfdKSfkqUsONhJBDHobUKgw5UFi8eDEmT+6/Em5nxyD0dCld6lLjYBR6W0bDWzcT4Z4yUXWagL6klRcW6sX7jUFKAjZSLiABmuAeL+GF7N+nJEwbg8XKgdEoP8txHPLjBRI3bzoZdXUTcLhOnqrLUtKJSQJtA4F9++S8kcRddubYMX5+haZ3g42R2wJ+Nw6ZEAQEgI2bLsGWLb/U3MfbY1xfKhxyUYnlXq/oUWN9dJ0iUiV4LZRXkAXpSkpqkTVfNNKYAUpdI2X7mUH1MOOLsPKK1KIuW0dokASH3XyY/TtvzHRz+jFCEprjnhleY77nJETfDZaDeN22OhmWMupcZQAsy7OCVzyIIxwcZmdb0JNLz+ufaTmMnRYKiVcHx1hrkWtAYPCPbmMcltPtO1Fu7UR3nn48PxD3yHg5dcimrzjmGLnWSMdhevpy2NGeqn+UZd4YlE7mZqv/9gVOpxMnndS3qtZSCAKHzdvkBf58udJ0cgaHvrwDjauvB8Cgo4gsPkbi3vQHDz74IC69lC7mduaZZ0q6KCFvG5joaw5Ow+G68dhFIfju2JESrfRo1LaiIcdeCJZl4S7WTtVOZOD09BTjUO1UlSFGK364ebMY5hjoUFM4rOTMiP0dPWEYRs8pR6+vUJbCnfAkzTilCu5uLUK49m9EMiD3H9AWBU0XEp5K5ghNk8IwcXHAE2sICpL/0pFfQB/vcwihKzMYNGgXJk760IDsgBx9MaL4PggtfqeNGVJ1bDPwIWhoFRpEBJssNfCwvVhjES1Wo8aMlQEOsW1Yb01NvtJ+00i6ADDeosy8MY5S1geHkfRgA67L9yTx3Zi1V+bNWm9TGyy+3H34a3YQjUJ6VusAUKoq5qYN46Rd9feXZjyFHPTzRAzWudIDwzBwuVxwu2mVkJXX7Xsl9pikCjco3gJiAUwdlJXR5RAsFgtGjKBX0Zb+ttKBU8l1UHoXAFFDprZ2GnhCoc22tiHwdqfOHQjkoK1tCDwHUiGxukMTMWvWLCxZsoTYt9vuvhkAcNr3tD0QubnaYoIeD/n+9PrEd2TPnvQLAyYmc4ef/u7MOnYS8stFw6X+8ERs3LAM7e2DsW2rKNw3fkEFct15xGwwvVR7QK61k0BZGX0h0tlprlSBJZINf28e+do6E+q+1aKB7a3TryifgNa4n5XVg878fGJbdo5oiHx+3CLq8XaHDxMm0DOS0oG8vBbY7eZ0zbSqkGscZfqI77Qx084aqwBNQxPbhc+sZDXNLiblJnvHti7573pWfCiLNDKd3BIOTYmFwUc2udu0S5JBpSRxJlDA9C8bBgCKWfo53nSF8JfsIPGRW5T7rOzzjCy598ifLTLf91li2OggG0wtFgHlnPFJ98QTtVVPi4rMCp8ZW00oM7sAsXaSkfN40kRuZuM1X4zWNtHLckogECDXako3rr76ak1jxgzkE6T8fkjTpRPw+egGczis5Kkw2L3rWDRtSKkPd3WVIzc3FxMnkjVXrDZxlZ1X4oTbrTZYEs/lGWdoc5AEgVMZBInaTQCIxlh/sWG9yAfJ8RI8KxJuks2RCpP39hZg185FCATE75qd78Dcs4ejo30oVq08n3idFV9qlFAwuagnFZzUQl7H1CRJXHVpDSmAYNCJyGHRgPQcNM5ZcufSBf0AYNO0qcgmZBSNGLEGOTltaC0tlaVtSzF2rJqEeyShFNZLog9cHbvdOFk4ge+0MbPcZmxQp+EL204ECdkrALDKkprQvGxA9u9athVT45lOdkb08Gzn6pIel8QrNNTGYDqBc2NElVjLEEkHaq1RtFJE/m6NXQ3OJupAdBOs8nBcY2SzPYJldnrYqZM37s7U41xUVppNr2TAGkitpxV8FBBDjA2iJ8+YvLcerFb6ZJUwYswUatPyKB786H50NIxLrq6l8LvUhHnS2c0gkam1ePFiU8clIPVqmKkD9NWKizV1aGjlBHzufdizex7q6ibA6y1Bbm4uOGJoQI68PLkxM2nSJFxxxRXxtjzqcfmU1frBA/pqr0EdccImjWKYZtLBRw1TE3kHVcZDX0LifHYcrkvtF41akeMZDa1VuJk+9BW9vTRibapfypT/UNdQsIL4Tvpb0yes115cjKmEjKKcnE5Mmfo+AGAMxWjJyjLv6T3heA21cJOYPoNchsBiUXPrcvOaMH36O3ATspVYwgLRCL7TxowSekPwvHnG0zsbuU70UDgnH9u2wcEysDLAyblWvG1fh9XWfckQVALlVhZtjNp7lPDuaGGQQkPGInA4IUxX7KyMFeCyoHFhsLPsomgdKR6aw4TxjKMcXzoi+GcOnUA7yNqMLI1QVjN/ZDwDJAhsBNHBfS+XwLMRonpyqt2YHg8ADB8+HE4nPZulL8aMVsmDVvTg4NbTEAplq9rC8bCZljEkTUU3g6FD9bVcSJDeG4+nFB0dlbJJUwrp5K5HEKatzIPOZrS2DsOh2qkAGIwbNw4Wiz7R3+GQqxHPmDEDOTnaz/jJJ5+M22+/XbV9186FCFKqnEvh9+eioYGuX7N/3yxs3LAMO7YvUjcyAiwR8RmwB+QZXcpff848dajlwotET4wgANndYpiwtnYaVq08Hyu/vgAMb4cjqB3+7ehIg06QBhgwyG+bgWaKynECXq/8+3ft1M/ma9+1FJ17tD3GfQGp9tNAIewznsknFc+TwuVSSz6MHr0STlc3JhOylZQaOkaRMWYk0BuQzK4c37du1myviBeWDMSF5mq5NggQUCIpOPm2fZ3quI3WGmodqAQGc/IHKMrEUM3T9S4WRybBDuOu6ux4n0nD/RxrHYIssMYRJaoChwC0szyKbX3LtDoS6Mndi45AA7Fti12f9KyXBdVRYlwL46yzztI0VMwYMQkEXfQBkaQ3I0XI3oGQI/2/Hcl7dvHFF2sec/XVVyu2sNi543jU1pK1YTo76JkpbQoSrTSTSQssyxryzJCOk4LRUSvui5ci4Hfj4IGZaGygkXBZ9PYWoLNzMDweuWHBAJIQAYOtEk9dRFk8kvAIJvRzBEFAViCVLRaN2hGL2ZDXqW1AiMdyWL3qXN39+gJPo2jwWmJO9LarU/fz2lMqzU2N8hCloCjR4KlRc5bC3nK0brkgHV0dMLz2U/X8kkAs4kDbFu2K6H3B8OFrZWEkpZYMq/DmH3PMXEPnzRgzEsw7Jr0kum7WT13BRhHDKIf89oeYCL40KIr3ln0deBMVwAfFtMMwFo3UUS3oTaMMoY9dHI8/ayjwAsAUC9mQ6A9OPtmswBUZFflr4dPzrJis5+N0OmGzkScrl0s7VNAXz0x/4M3fYUrV2G43VpuH1P+RI+lhEACGSc8JxDQMlGBQ7omKmSiFoDRMAKAiIPfkKgvoVVSQ08Fp6JaQgANBtddMyqFJIPEdSERaJZSeqGjUivxi8f6yvAXdnnLs2rkAnZ0VOKTgmWh61eJfO6tX/n0tUfE7WEP5iMXUv0sitNPfQqE0xLyp++VpVht71ijd8yUonqOwl/Bb9iEjpy8VrfuDtjo6L3HfW79ELNqX2mbq78BKElUqKuXp6XabnB+Tr/A8CVB7JknIGDMSpIuEKEUdpbbTRksNscr0Pq7JUIaUnwlho6XGcD+4fiptLgupV7o2RFHJaq/iHYSMsSLWDzBATKNPUyyNGGcxvvpnGAZnn3225j5avJkf/vCHhq8FALvzD6a12KXFYsEll1xCbGMYxpBnJr3GjN4zaPxaVqsV9913H2bPVmcTDR6sHUYw+p2WLVtmaL/6wxPQ211GqYOUupbfn4NmDT5JAicdv5TaNvVE8gVrkAAAaxdJREFUpacnZdifPPUyogGkDQabN52M3bvmJ7OYuGgqxNbjVYcEPF3iJNsiETykodcn547wvBVLFp2O4y8fA6dPNFba26uwY/sJKgOD4zhYwuTJP2HE2YPkkIUlko39++XPRm3tZKxZfS7YqKPPldsBYOMGjedCYmywGoYLQKhL1qM/V/Q0TNHdR4miIm1OGqfj8VWiX4UyBQsYk2KtAOB0elTbbDYtQq/83ubn900sNWPMDDBoGVONbCccDEM0XP7k+NSQPslmS63sc5EGx2RsjO5eN4IiQX3uRdYDGMHR03BHca04TYPga2S1mEBWljYZWBAETJo0CWeddRZ1Hy2SpRbBlgQHE+53sUspTjnlFOLkNnq0dtFGp9OJYcNEF3lajRmdU/GULDoSBEGAw+Egfr+rrrrK8HlIoZzERDljhlaRxhSiUTs2b1yGxkbCIC95FTesPzMp16+FnHw6lynLLZ/wpZ6ZOWfoGxeA+jft6SlGW1t18rOrJ+URUU64zU0j0B1PLe/tLcChQ9pFH7u65Do6tmABctwujJlbniS7KkHzJlokZUqKBoljB22RxgBobZGHeYKBHMRiVuR6xkPrYWxrUYeHpGAb6N72zj0pbSZHQB5iU6ZbR2tSekOh7gpVnzw1aj6lEBcLjPjzNPsoBaksgBRDqzYbPhcAdO0/nridj1lQ9/ldxDYA6KkXF6/+VjrfijZF2R3q5BOa6CPQt6r0JGSMGQnKy82LYulBa32bAwY7OLLS8Lr8RnhMliLQChUN4sWV3JCY2RRlEVZCYYYKzosKSn0nOyI4xnoITkq2l1m43W5NTlPCUKF518INPmTx6cuMYHVCfEtvnKDZrsTo0aOJxsjxx4uDEU0n5+67706GcWjGzN13342FC81VfdarHxWxaXvkpEgYiiQvjJZ3QuktIxmjCQPBjCFH4zM1No5BNGpR8SO0IL2ucmIfMkbu6dDTOmINlDVRwhJxY9o0ceLp7pafv9sr58iFw9oLAo8nFSppaRkGR6AcJVU5YBgGV/1sfqqfMRuyu0fA7RspqSsFsJL3K09SLbt4SA6Ou2wMGAnhuqA15YmxBYsAMNi0KeXlyskRF0lSzxMJTWuUnCk5GI2JMibxJCkzRLsPiTyNqSeJ3jV7oALBrsHxtjmqc/HhbPR2pjxxkYC2blBfYScUedSCr2kimtap09VrP3pQMwurMX5fhZgdEUq2l+NfRXC9oM60KyxUz2nTZ/wHbncLSDNiSYnxCIMWMsaMBLQUSKMIudXej90cmfuRqKy92kqu5VNtr0EnhR3eHxwbGUdtc04rwcIwvd1MtpPVgFBSIWtcS6C8vBynnUaugzJ9+vSkIUqbMFp/swnNT5CzixYtWgTAXDHHYou2RlGW27in57TTTqOGkhLfZ9asWTjuuONU7VJvxaRJ5JV3Tk6OSgFZCSU3JZ0Bq0R4aexY4y7v8ePHq7xlF154oWo/JQ+lPwiHnVi18gJVyCPh+SJB+ptddNFFsjbOIl9cLFq0CAsWLMANN9xAPFd+QZ7JHgNWzp58Bqg1pOIp5t5udQJAtnc4rJHUxJsgAdfWTgGE1DPpdKcMFYa3ICtQgUkTpsrOdfZ5Z8AWLIC7c4JKNbdqYhEs0Ww4eivg8g4Dx4sG+PGXj4U1mgNnz1CZeF0ixZ4BA3fXeDTQCMw6CxQuRjaG2nWqqfuaRHJyrkS5ue7ze3D4i9tlHh0pPHslmUsGvQ11deYWPeafdgbdtep3X8n5UbVLSlA0rbuCuM/+wedhg/tnqu3l5eQs0PEUQb+iYiNyD/rIGDNxJNI777vvPs39tAa2B2+4SLWNpkPjYf3EittShBlzwkFGuDZaGUsF54/GKJ7unTKT7ZTDGCtzYBQnnXQStf7Q9M3FaPjRKoSbeiHw5l/3MWNEV+rSpXT+g1lI+RF6MOJVsFgsOPZYbXGu6dPV6bGJiZMWDkhASUbVqqGlRG5uLtWQAlLGjBnviTKNGQCKi7XTROfMUa+YAXJ4ig71kGgkLR5Q32Ol18lut+OEE06geoAvuvhClJWV4YQTTkhuS3gjE94XJcbMKacadN1x4yXbJXpP/IRwhyWSg2rbXBQ1LwDDW7Ft62Ks/PpCsWaRjkm78EK5B2vM9CHI9UyAPaxeybMcAwYMcnpGwOlPLfpGzhT76OwdLNP8CUm8SLZQgYZxQH/fD+yfAUuUTJ7fu0cdFtq/J5U1kzDGRs0uw5LrRIODjzjR2zIBoKT0CxFtLxL5IHPLBpLXQ/f8pP5K7nXNhw+hp36qep/kOShmAqHvehoxdkrNpcrKnX1UCpZcu19H/w/h+uuvB0AeRAEkV8WyOjAK6GWdKLGTq9d0g7pN2uG0vUv4XGRNKsKgJ9NUmdgAltj2arYvshrXcBlcOQjhj5sQqiN7Q/jeCIRAFK2/2oj2P5MVmbUQiYgvYDrDjH3xGBiZ7K+88kpqG8mASnwns2RT3kRdrJEjR2qSr81c+7TTTkNlZSXRC0VCgaSSMIn3xHEc7rrrLsMZVSQYTYuPxeSeVLP3vLCwEDfeeCMWLFiA448/HkOHDk0aiSeffDIWzDpBdcwxZ9PLPITiOjTHziNrnfT0xEnEFhYMGHDRLABMslq3NZyn2V+p8m9fMO/cEbBYxYlWq/4Rzaj6+iv14lEKIi8qDtavNowje0+FIDDwdImh6rPungarjetT+M+ob5OLmJszWJPUA1o/pBpLIc8QNK2lc9eiIRoXU33uefNf0exNdjY5C3LY8A2oqOifwOh3ypjRGiC1yKEAMGGCaJ2bTQXVQhfTC4vGT2A2t4rmmTkuMh5cfl9S7ESMjppLITWCKs54JlDME4JvZSPantUvMBfa59H0EpBgtm6TESgntkGD9AnYyklz6lT1aqmqqopqcOuRpGnQyyjSg5bhpvdeKTF9+nRcd911Mi4GDSeeeKLsnpE4Veeddx5cLlcfModS0DpW6tEKheSk6P4QshcuXIirrroqaaDZbDYsXKz2PNmz5AZc/WExTLx5kyhDMGXKFMycn3ofpCUqtm8TjaMTrhiH7Hy7rCCk0zeE7pnR+Fon3yCOk5Wj8uT9dKp/m3HzxXt3yWPq72UNu+HyVqu2S6Gl3qwFWh0rJlCCze/dj9oVYj2tipF5AACWNfY7hrySxZBkkRrqJr/73roZCOwmh84HGoLi3ik/S0FMOwcQDfaBF6RR2oBm6BjFUTVmqqqqklyBxN+TTz4p22fr1q1YsGABHA4HBg8ejJ/9TB2jM4pZs2YZGmAuuEAtdGR0YMrScEmrzgkgwtB5MbQQFQ0F7nyMjKq9C1bBAseIPFPnkkJLbO9IoLE3pSirV7YASBmeRjBx4kTdEAwAQ5OrFC+//LLss5HaUErvAY0jdNlll6GsrAyXXXaZbDvHcTKD/d577zXUVyPqtUazhZTQCw2ZxahRqdCGUpGb9GwYCffpcXlIKeWAyIGRllJQGj39MaBIMHK+mprpWPHlpUn+jLLm0/p1Z2Db1hOxccMyRKN2WCLZKBqUjSuemAculoXi5oUoal4Al69Kde7Ee2IN5WLsMWQv5vCpJbjyyXk4/fYpsu1a46fDqfaoOUJFcPpTRrY0O0oJUtmJLZtPgrtLFMVr3yG+R3V1E7Bj+3FobRkOhhI6yfJVwxGUf7fB42jlDuSI9ErHydT3bVp3JZFE27z+ckT9fR9bOU57YR300KUoYoq6Y4JmbS+KEnantrGphnb+ao7bfIFaKY66Z+axxx5DU1NT8u973/tess3r9eKkk07C0KFDsWHDBvz85z/Ho48+ij/+8Y99uhbLsob0REirSaPGjNXAxGAUn1t3GN6XZVmccfMFWBBVD8xWcHCM7Du52S3oG2iXB40XW+sPbrrpJtPHHBcmS9sD+lySBK655hrT15UikZVEQsKzUVBQIJuQaZNXZWUlbrzxRgwfrk7vlRpERsOedrudykdKQKuUR6L/JE8S7b2ZMmUKbrvtNkP9k+Kss87CSSedhLvuukt17nHj1OR1PWPm2muvxfnnkwsgJlBRUYHvf//7qu2zZs2SfZb2Z+nSpYaMRDOgPQ/z589XbEn1Q33/GXg85cl6RNOXqCekhEemtFo+Wd5000046aSTcNkN52DRJXTJAFeeHSxnfGpxZOtz8Tie7lne++9fq7b5fAWwxg2g9h2nY80XV+FQ7VR0dg4CE7Miu8dYajwghuFyCsx5tqXOylgwFzUfPaTah4/2zZOaQNCr/XwF2tRZeQX587H3rWeovB9AnxwMAM3rNYqDUmCxRMFydN2arCzjhYVJOOrGTE5ODsrKypJ/0gH473//O8LhMF588UWMHz8eF154IW677TY8/fTTfb6exWKB1669WiRNcHoDU2IS0Vo9OR3mHt6YUta5mD7hLFu2DE6nE6zC9s3jXeAkP3POsWqX54QoOcxQHSvBKeFpcAv6/baAg9tEYUglEiRcEgol+jmdz++AzZIa/M6NqKWupavlbDYLw3njATua50cv0y1RMJAGt9ttyCA2m0KtxNSpU1FSUkKY4OjIycmRcU+UOOOMMwxl+pWUqFeZNCNp0qRJmtekISsrC8cccwwx3Et69xL3nBYKs9vtYBhGl1NDIgErrzd06FBUV1dj1qxZVG9Of0B7fvLy8nDnnXdqHpvlI7/juSX0d/ace+WE8vz8fBxzzDGoHFFkylgxCi6ahaamEQiHHfDUisbzKTeJWUW0ELoAMfOmtbVKvl1gITXqOH8qlFzUNhdcLPW9jaxTF19Nz/I0hD6pAWuDj9pxeMWtxLaQtxSt29Q8NqezGnyYvMjhIBq23sPqRALlNinnxteoX5YigdGjV+rs0ffsxKNuzDz55JMoLCzE1KlT8fOf/xzRaMpyW7VqFRYuXCgzLpYsWYI9e/agq4vOuQiFQvB6vbI/KaIW7ZBBYWEhtkVTE2B5ebluQbiEequWMXPeBfIVYKuOeq4S5RV0gmqCxFp8k3zyqOblhlvu0tRKbHisFGeGZmJWVJ6We1poOiZFh+LYyDhU8MY8OgxED1BfoZUWLTXQwoe8iEZSz0geIfVSyoGxRrX7pJwgSBLzemnNAFBdre9yJa3ulRg7dizy8/N1PSU02O123HzzzTjxRDXpk8aN0QtfTJkyBQAwYgSZbKr12ym9JTNnzkR1dTWqqqo0r5kO2Gw2XdHBhJFDul+A3EurLIehzJLiOA5XXHEFTjnllD701jykv5texhYXM0eAHjWrFIxBrkhfUFDhUhGIbcFC7N83F2tWn5OccN3J1Gj5JLd7t9xTGPDJFyGCwMiSKxz+cljDucnQUwJzzxqOYVP1Q6HlpsP0invXD2OGp9TkEmJW9DaRx4nOvSdBIJQiiITpnpHd79yC5o0Xo2WD2uuSCNUlry3x3oR60pc4cdbZfRd3ParGzG233YZXXnkFn332GW644Qb89Kc/lQ34zc3NKnJm4nNzczP1vE888QRyc3OTf8pBnFGQkEgpnY2ukVgbGYyS6jHJTCctJLwBWumzytVdF6stTKaCxtiS4InYh7oxN6o9gCfAgkGR4JYZC4WXjkWpkIdZ0RFEEb7jw2Q+CkOsn50eKL/24shkcByH008/nXrM+eefj5KSEhwfkQ9e46Lyl0UZUiQ9CyedJOpK9LWqcwI0gq7UWLfZbLjttts0lYz7CpraboIzQuPoJAw+Unrw7NmzNQnXSmNx2bJluOKKK9LOJyHhgQceSHpUaR63RBiKFm489dRTk/9WPhtH4jsoMXPmTOJ2Pa+fi5AA4OqpUoWSjJ6vLxi/MMXhmHOmVphHaqSJ/2YVIRk+pjCEAkqDRO6ZscScyOucDHso9RyMX1CBaUuGGlZjNgfx2olMKC2VW5qxAgAtmy5Ax+4lxLb2nacSt4sXJF9vy3t0ozcWzIVn/3HE8FfYK+ffSI2ZaBoFAtvbn9TfiYK0v4333XefitSr/Nu9W0zBuuuuu7Bo0SJMmjQJN954I5566in85je/UWUFmMX999+P7u7u5N/hw/Lc/HxX6uGx5hQSCxC+f8dC/OSGs3HT5RcYerETA5uWDk1/B7/8rFzMj5DDMVKC6mgJCZjTiI1KH/iSW6cAALImaBNVh/F9y/zJ1oh5A0BgezsuGEoWo1JiMF+IBx54AMOa8qj7jBs3Dlcvvgj5gtwLNyc6EldffTUuuugizJw5MxkOiLT6ETzg0VzhplOgLYGxY8eqvBcDVTCSZVlZ+Omuu+7CTTfdlPSS6JF1Sf1aunTpUZnUaaCFg2nvpZ6CMM0bBRwdY8ZoDSolppyg9spdcdvZKKxIvR/ZBRLvzQA8gsMlHhDl7aaFcRJ9EpTieIoFqZaxMH0peRGS8DzllfZBHwYpdWASgl1i2+Kr44sphWema1+KqK9lDAgCR+W3+Bo1tGEUqPnwIbRsPg+eGuPhZyXsf5Z4rSV98talP6QKAI6N5h7CtL+Nd999N3bt2qX5RxtYZs+ejWg0itraWgCiNH1LS4tsn8RnraKQdrsdbrdb9ifF9KGp0Ml1l5O1CnKzrJg2JJ84yJG0PoxMQHoZLUuWkC1wALj44otRUFoIm6BPKswaJMmw0BiVsiDxCAzSDqP1FwwYzdpRkUM+5OyJoTBXzaOYRzDghK4wfCvI6soJ4Tzer3apsmAxZMgQjB49GsuWLYPVakXPinq0PL0B7c9vg7tbTkY0m+ath/vvv1/2+YILLjAp6tY/HH/88bjpppvw8MMPw+12yzyfes+w2SrPixcv7lMfBwILFy4kek0Tnpm+aAwdqSrlSiSI31KRRD2is9Uuf8YYhkHJUPm4eN59Ka/P4LHm+Ux6kBJ9ldoto2aVqQTuxh5TntSh8dbJPVKxuDeDjY+H0urOCVVdBixmnz4M1ZPSm1EHAGfeOZWo8VPzwSPo2HMSmjdciht/uwjDp4nXVhpbQU/KEAq00w3mnsPTEeqmZyUBQMsmtTK2v03unQ95hqBr70maxF89WPakPDYyb1KaaispEegyly2VdmOmuLgYY8aM0fyjuXQ3b94MlmWTRMK5c+fiyy+/TPJBAOCjjz7C6NGj+1V6wCqJBZcUm69VVFVVhXo+T7YtMSFprdb0Br+5c+dif0ztDp8SrcKoUaOQNamYqP0wulLuJs0/K8WBIe1/xonLMDhWiCnRKs3+mIWW34IBsDQ8lUoSTty1IR3yVUoRn4NCQpFLIUofvANbxVTuaBdZhTiwO6VnEPUE0f3fVG2QSK2cUS/lP9A8M2b4H/0Rb0sHWJZFaWmpJlmWBimxGlB7CaTHP/DAA5oZUEcaVqtV05gpLi7Geeedd6S71Secf/75uPDCC5PhT8C8YOfiRWq1a6fbhgsfnoWTr5+AUbPSr71UPFhSC4nAx7EHS5DdPQJ57WI4U+r5CHsrsfWT23Fg/ww0NoyGx1MGp28ILBHxnN7aYxAKORGJ2HC4LkVInXFKFZ08LNk89yxzoabK0eL8M/nEwTLPTqh7ENq2nAc+nC2KESbeCSVnRpLc0bJZbYwAwJ43fotYyA1fo5oX09OQ2ta1Ty2mGPHrG6Ocxdz0H+VcyH6fha9xInpbJOH7fir30nAgoE/vkOKo+YdXrVqFZ555Blu2bMHBgwfx97//HXfeeScuvfTSpKFy8cUXw2az4ZprrsGOHTvw6quv4le/+hXuuote7dMIzAp5kfBFVG5NJww0l8vVpwkrMVFkQa0tk3gZGZYsY2W3yK/Huqwo5sVV17BYCVyz5V6sqfNn4vJbr4YtXjySNZAaqYXjkjwaujnDgYUdVoyMkVfAiW82VWFgLQ6bJ8J2LxeNE+/7tcT2jpd2wPtJHSJtfjQ/uU7W1vOZPCQpFaijydonBPFI4cpvE8x6GpSeGqkwoNGU93TDqFpvAlKv7fjx9BT+bxLsdjvGjBkjUzxmWRYnLpQbl5VlqdCS9LsXtM3EMcfK08oTKKzIxvBpJQPudSKpBzNgkBWogDUqhr7cxfKFTySYg8bGsThwYBYgMHD5qjA3zr3hwnlYu+YcrF51AXjeAqekmjhtWJIuTrLz+7bImH/uSFzyozmYdJyci6e+ffKp1tcwJflvnlIGIVUfSf1bNHx9s8meqqHUAlKCYRmZB23L5O+h1LsE9V/dJjPOYn0RzzOAaMCcw+KoGTN2ux2vvPIKjj32WIwfPx6PP/447rzzTpmGTG5uLj788EPU1NRg+vTpuPvuu/Hwww8bIuRqYcaMGZgzZw4uvdR8rnwC3zthNA7HUj+iNFRwzjnnUI9TFqNTHkOSFeIlbyPJnLETJo5Tw9NxWXAhqm+Zg7wz1G5Ma6kLpXdMQ9bEIhRfZzy1DhCznaQwkrp9bJyIS1sl2QVxYGYVj6QL5EGmd0MLcTsAxLxhxHzkysgJeD86hOBu4yrEAN2rkhCUS7dA3JEGaQLTMsyV3p1BgwbhiiuuwB133JHurqUFyu932WWXqbxNekinAni6UVQh92BecuklyX8npA8sYbcsLflI45izR2DisZUoGaof1uYMpH/nFqsNAS7ihLM35dVx5ZGfYSmPyAgd7uTrxUVb8RB135258jG4bFjquSLZhbGQ/DkKeeULTiXBWQ35vdn/zs/lzQayp7JytBexBeVOlR31X5DKlqi/4L63n4a3biY8B/pRQsdkBlh6VZ1MYNq0aVi9erXufpMmTcKKFSvSem2LxdLvVfT3jh+B13yjsWsTuRIzDbRUUa30Vul7NvnyBfjola2ydpKBw4EFBxaW4ixqiqW1zIXCS4xVMi5/YBaafip+11IhDzl8FnpYsRhhgpdTJLjRBXWG1rXBlBuUJ8hZD44VocqkyjCNL5NA2x+2arYDSBvJUc/TRxKTMwr/5lb0fNWAwkvGwtKPkhRGQDJmrr76aur+pFCVkRT1gcTixYuxfPlylaCdEtXV1UTRQT1ceumleO+99wzXjjqacGannheXy4Wi5nk42mocWqRZGgorXeho6JWRftl41W3pI5vbMRkRexecvqGyMZEmeOfKNeeNGT6tBBc9PBvuYvX5hk4owuq3DiY/S8fcSx6bg5cf0p/rpBBi5rzl0WBe8t/+9uGGDAG9MFNeiROchUXrIX0xu1jEAc6aCuvHQjloXH09XOVbkTecPH+73+TgPZuugG8W35w0hG8ZGIbBWUtPxNy5c1Upr/1x0e6NqVf3Um9G9hh1O5s18DYp57bLwlEkYvHImL44HU/wzBwXGS8735mhmRg0aBDOG9d3gzPaZrzqsx68nx9G17/36f6utNRtrRRyPXS+sgeReh88bx/o8zmMgsS70Kpb9U30RM2aNQt33HGHbgX0BQv6tmIsKSnBFVdcobn4OFqQPp/Tx6q/34UPzgUDBrNOO7oGp1mc/8NZKBuWC1soFXbI7YqTfFkmWdXaFsmFy1dFXNxd+7T6ftik46bCNTP//JEgoaDClSQlS1E0KBvzzk15wKXGDMl7pEb/MyUbV18Lf9sINKy80dD+egU0nW4bllxvtDQMhZekUfOJ9WmcLZ76XffZ3bDvNDafZoyZfsBms2HJkiWqSaw/xozdSSC76jzoXM6R4SeU3p7SGcmVlDhIiOVpVQBPIFtQr4aUBk6R4MaVZ1+KQe70kxBloHR3eJH4eyb0ibzv16J3TTPGldOzDgB6WjDteTDjseGDdLGrdMHtdutK+0vxTUrJliIvL++oZRp9UzBisDrVuWhQNm557njMXPbtMmZYlsGCC0bCGslFXscUFLbOSWY+MQwwYrq+V9dOqP8kRTpEF6REYJKh4G8VywvUfPCIqo1htd/vsC+VqEJT/fXWzUbdZz9ATOKl0QLLsbjwIW0PprvQYEiSYrRolUbobaWrvntqxOQBf9sYFLxgbLH+zRyN/sehdFF7bKkV7uNnq1OBbTrqk8qBm8uxAhYWjI0DYzOfipe7tIq4XWo0JfRuivicZO0mEndGWXF7dEydZmgjiPMJkRh8Xzca7nNfQJvulk5YhFNPPRUXXXQRgvs9ye1Diwfjlltuke0rLXxoFLfddhtOOeUUU5oh4cM9iHm1eUDpwLhx43DHHXegtLSUKqL3vwBaaPCb6HExCvk4MFASlgMHUmXtBBIhEWvEDVaiOaMkCRuFsraUza64dh9un8Wamk5Jxkzd5/di92t/JFbRDnnkC+JIrzyrtWmNWBuudetZVNVfEk66lk5qZzkGhZV0NXwzt6D+K7KBFeklZwvnvsph25A7DJ27vcCYd+iocWb+lyEdKKdOnYra2loZR0epRtrkTq2iZo6twnLF+bhCuTfjnHPOwdf/+gTNrAeA2phhOBaVj8wFGHIKpB5yjh0M66ActD+/jbqPE3YZFwYAXHDg9NAMdLA9GDxlOLInlkB4qU7+XRT286XBhSrSLwBAAITIwKT8JRBp8RO3Z2U7MWOGuHqtf2GDrK24uBg333wz9u/fj+rqalU9orlz52LVqlWa1y0oKNDldKgQE9D00zUY9GQ/CHUGkZeX16eCnt8GXH/99fD7/dS6UFLC8w033HCkupUWHK0MsnThtNum4PUn1xPbVEVF51dgyLiCpOhf5eh8NOyRE/rzy+np6uMXyBdVVZOLMHp2GfasoSvL66FyVCoMNm4eTZOJPB43rb8cOYM2Jj83rJK/f4GOEdj9r98DBnTGpBg5oxT1e7qwc4V6Yag3N+QUGufoBTpGoO6LOzDk2Gdk26N+svI2280AeUDH7iUoHPOB5rl3jL8KWPtv3T5kjJkBgFQcb9CgQTjjjDNk7Uq9kovnp1b3JPe4cv+JEyci/+8evOD4hNoHxpo+p1vZ/amJl7GxEMJ0I6NEyEVJLBfYEIKTCcOvwbK12+1wBMnu34Tw3UCidy154GIs5D4njKuSkhJiUUVAFD5cvHgxenp6sGXLFmIJgHSi4++7IIRjKLhgNFgdV3oG5oT/+iKkdzQxdOhQOPyl4GLOb2eYTeOVdxfJJ9a8UieGT0u9g8OmFKmMGXsW2StNqtLNsgxOvGpcv4wZhmVw428Wobs9gAINQ4oEZfHHiI8wvpg0ZBJgKc9CwntUOCgbHfVqAsvk48i13BJw5dnR60mp9ftbxqFpxVUI+AyEMeNdatt6jq4xYxSZMNMAIVGHh6R2rDROTp+sPcDqrbgGZOCSdJFzpV5+9wlkNzxHSH/0r6enTwPa/fZvatXpoBzO6Wnk11BWLB0v7UCsV60DpDqcZZGbm4uFCxfKykykA1IjT4jwCGxrR3BPFxofW42er7QzvDLQB61+1rcBDMNg7tTjMSh3DIYb4JF806AVZrIowuV8TL6gGjVTnXxw/OXkTE0jw6VWNXEtcFbWtCHTHxjRx6F9X2v8ni6+ilxKgtNZEHOqRR+D7qZjEDZQeLLbluAfpm/uyhgzA4Q777wTd911F1GpWCk9rmeMLFy4ULXNKnlhBmYVJrFmJOe3UJj5ZffMAOc25+bW8r6QUq+zj6VXVGVd6fNKhGq6qW1dr+9N23Wk8K1tQs8X9br7tf465YpWGsXd7x5U7p6BSSxevBhDhw7V1Ir6JuPYi0bjoodnJyeqbxPySp0yfRYltIi+Sm+LK8+O/DKyUaFlbJx9zzTMP38khk4gh0cGAtWTRU++p+YYAIC3bobhY7UMjjFz4wYeYX6YuCg1lmrxZrTQn6rqO7P6J3xLQsaYGSDYbDaqwJaZgoXl5eVE5dlEYcgBg/QF0HlKLMVZYCws1aNBgxAyrjFQfv8sZM+ip36n057rXdVEbQvu6qS2aSHmC8O/rR1CTB2i861qhOfN/eh+rwbRjlRKeahWbVRFmiU8nwEofGkU0jIP/0vIycnBVVddhYkTzQlJZpAenHXPNIyaVYqFF6qJ9dKSA0rOixKk8eD8B2ZizNwynEjxRABA+Yg8TD5+8BEJ0809azgueHAW5p8npoG3bLgUh1d8D03rrjR8jiknkj3lucVZOOEK+vfUc4iMm68fjmUNjve1Hz8g++x83S3rQOPqawydR7c/aTlLBqagZ8xICcJz5swh7sNwbHI/ZdXldICRCCrJXmxC13OXxmOkOroFCSRKGpiqDaVXR8TCpjfUlGa0PrsFnX/fhZ7P1d4XqYaMT2JIRZrUAoQyEKhLsZ6Bz3gCgMpK7ckkgwz6ApZlsPjq8TLPQQLuoizc/PvjcPPvj4NDzxNLGIqKh+TghCvGIXuAxSeNwubgUDQoG+4iMaQl8Fb0Nk2SlDEATrxSW9S0aiI5W6hbqrNFuBfKTad9T54hZaiSuEGDL9hZjYPvP5raoMhC99ap5zhlNpcRZIyZbyASYaWKigpMnkxPw7vppptw5513agqb9RUWE0z2hOFjdDWzIDIW54RmY2LMeBos57JqhpI4tw35Z45AFuHlNhv+AtJPQI51iuqYgR3tsmu0/UmeMeb7qiF1bZ0+kPpIMoDS+V2uv/56nHfeeRljJoOjAoZhqOOMVJ6fXMXu6KF8hDp8ZiRMM1LDGw1o84yS1yFssymEVoeML0R+WcqAkdaaogkImkHYW4nsD1hYaxkEDqgX3zGvPNTVtXex6WtkjJmjAL2CdpMnT8btt9+Oa6+9VnM/i8ViuraMUXDZNpTcPg1l98rjt1ZCfNUe18Gx6+jhJMCCQb6QLR9wKBlEsuMIxekScM0oBWNlYR+u7kNf+DSdr+4xfYwhSOwK34p6hPZ5qLv6t7dT28RzqY0UpeESbQ+g6Ser4f2kTrVvX1BRUTEgBRm1qqBnkIERXPjQ7NSHb5Ytg6LBajHUwWO1K1vnFDp0v4bVbp4bZXdaMHWxeiEpDVlJSx1MPp6c1eR0mxtX3W9bUPwzK/ZXXaBq47nUgjPQPgwCb37MzhgzRwFGqmrn5+cfdYVVW7kLFoUCpIWQtZRYYeSeRJbzNwLnhCLknWm+Vg4gGlhMoigdYYLPP8f8yiKwpa1PfSEh5g0Rtwf3Ugpdxr9DuMarfWLS/K8wCrrfqwHvj8L70SG9bh41hA52o+HBr+H9+Jvbxwy++XBKPLBHMqPICCYsVHsyE+ElGkbOKNU0ygoqxO94zVPa2lNSo+mW547HtU8vVHlmALL3SAvZ+Q5Zerweut1VAACBUc9r/vaUunrD6r4Vks4YMxmYBs0Do6dzQvLqJMEw4IN9KzpWeKl2XNlS4gRnIIWxr/BvakXn63up4RzPfyRZRgZIu23PbwMf1r8Xnv+qs5dIBOMEQnU6xtFRQtfb+wEA3o/T4z3K4LuLc38wA2PmluG4y+hS+UcDBeUuXPfMQlz647kYPq0Y5/xguqHjtEL3iUWkHn9o6MRCnH77FFz55DzN/fLLXLjokdm45hfGhDkZiJXEjZCFAWDDtHvBMyyiFjUfp2XjxchezsL21Eiq0J4eMsbMUcLMmTMBANOnG3uov0koujolL513lna9IilsBFdrAmyWBVadlQoNsmrSBIOCYcS6UiW3TDF13t51+gJaQpRH56t74F/fgoYHvkJgtzrbKbAtFS6SZyORzxmu9VIF/YCUwULyHnk/ohsEbc9uMWQkHXEcAYHEDL4bKK1244QrxpmuiH0kYHNYkFuchZOvn4iyagNeEJ0Yk91ggWGGYTB4bAFcBK+6EgXlLqKo4MgZdA+MK9c4J7Ern2xk8hEX3O9a0GpVy5AYRUYB+Cjh5JNPxsSJE7+VREqGZVD+0BwgxoNzGxs0shdWImdeJXpXk9OeHWMLDHNuNPtG9A4xYB2cpjFFQtcb+4jbY95wklTc/UGtrK3jpR1pKTkgaBgdvWua4ZpLFqaKtgcQ84aSv4sylOX98BDyTlULOR5VZGyZDDIwDS3jRKmY3F+QyMrTT6kCAHS3B1RtNEQ57X41l5Gzd40g45k5SuA4DkOGDAHHffvErQAxu4hkyORfMJqwN5B3yjDNopeOkflp0XZwTiqGRaneaTBl3Ci63kgJ55HE/aTQCvtoQqPL/k2tCO6k6930bhTVkwVBUNW38n0DVYKPRAHNDDL4toFUrFIKreEy3To5yhA6yzLIKxHDRXvXaCu9S7FjPF1Tpr9rmowxk0Fa4ZqqQQjr4/vFaGQxqfblGJTeOT15TNn3Z/ZLqZIEWoFKEjTDRRpvr1aWU/hwDzr/Rc+28r5fCwAI7ujQ696AQ4jyaPntJnjeOUDf55sY+sogg6OMBBemeAjZo1wqCVWdfa+CrpDmbC5euSYbAMuhs6B/emkZYyaDI4c+vmDFN0wydxmGQeWjczHoyQWwFBhzt7Iu4wZTLF5cLbjfo7tvcI86Y8mIAnToIL2kAgAIBsjSHS/v0t1noBHY2YFIvQ++leqqvYCxyugxX9iUanYGGfwv4dRb1VpjCy8chQkLU8Tbsmq52vy8c/uvDSPF8GnF8j7dnOpTupxAWybdIvu89AZzKtwZYyaDIwjzT7375CrY0phmWXonuYp1yc1TTJ+r/YVtxO3SiTdIIAT7VjYisKMdQjCqavufQ0zbCElkMpEgRHnU37cCTT9Zg85XBkj3J4MMvgHIISy6yoeLnhenQvTz9DumYOKiQWA5yfQtGVqX3TIJ1ZPIysB9xYjpJRgzpwyVo/Nw/a+PxeBxqXTvWaeTOXh6hSr1YKVUPachQwDO4MihDxY8q2Dsl9w2Fd6P6xDc2bcQioWQMcXYOZWeTn8Q2NwGp0a4rfs/A18QMtJmPBSmh0SlcE4jBbTny3pEO4PIO2O4LF6v50/Rqqzu35zK1gpsaQMu+mal22aQQbpw2m2T8Y9H1yQ/H3fZGJQMTXlbLv3xHPi6QsgtdhIrZUvfOd1SD30AwzA44UpyGIgm3Hft02IixB++90WfrtnVZG4MyxgzGQw4Cq80rhhbdO1EmcfDpai3ZKvIRtHl4xBu9KH115uQNd6cJgHDqVcLNkpMuq/ofHUPnFNL0hoacU4plk3uemh5aoOh/QRegH9TK+xD3URDT4jyaPrxagBA5ePzwVBIid3LawCIv5c0a6yrH0rKGS5NBt8V5Je5cM1TC7BvXQtGzChBVrbcG5Nb7ERusYF6SZCr9x4REIY5R7YVFmv/klvMlmHJGDMZDDiyxmjLdkthLUm9sGX3zJAVvJTCVpGNyh/PM1QGQRfxdyZn0SBiIci+ImoiZZErcCTrNymRe+owRJp1ik72Eb3rm+F5Uwz1kFLKeX8qFCaEopTUd8n+GgaIIAjqLAsGdPdNhieTwXcIDpeVWGDTKCafMBi9nhCKBmuIkw4ASIu2C344s9/ntdhYuIuzEGgwNvZlODMZHDGwNg6O0fnq7VKRJukTqWOnMFY2rSmIzmnpLdiZyCoygoJzR1HbjHiOzGroJBCuTakChxt9fTqHFFo/R8JoSiDmDakMGWmNpowpk0EGxjH/vJFYct2EtKdl9wXpqExeOSof598/Q1XRm4aMMZNB2sHGK7lay13IOUFe0Kzoqgnq/aUx3iPwIpbcNpWoPWNJwwuYQKjOi4CZ1GgNTQn7EDe1LYGCi43zSWI+sq5L6683qTca+DmiXVKPEv0AqaJyYEcHWn67WbVPx992pj5krJkMMvjGo2Jk3oCcN6/UCbvTinJC8WASMsZMBmlH2X2zUHbfLJTePg25i7WLT9oG56BQMhEfiUWFrSIblY8R6pSYuHbXv8nqwAlo6cSQYC3TiYfrTOyWfAfso9ReLxL8G1pTH/RuuAGDQlZ7SnK6cH2P+nS8gGh7AB1/2wmeIJZHSmVPXmd5zf+cwJ4QE9D2wjZ44pyjDDL4tqFkqBvn/mAGBo0xNv4MFDLGTAZpB2vjiNW1SSi5ZQqspZLU6zQL3NFAI7LSoAzj9K7RrtvEh8yRV1m7Nn3Nfdxg3XPoGkRx8CFJSrjiNgQPeAydQ3YMJbOsleB5af7ZOkS7yVXEVVAQAH1f1qPpp2soO387EdzbidB+D3xfpo+rlUEGRxql1W5UTyang2e5ybWbjjnHeF0/I8gYMxkcNRC5IFJj5giFGZKGDcW+sZQ4UXiZOXXKdE9OlqIsVD5OrnprHSQS/vQMrASkxS6V8fX25xXaOdKfg/B7qETvdGzEmCdkyPsW3N+VzJDSPJ8vjGiHcaL1kUZgTyfaX9oBnqIpJOUIZZDBtxkTFlZiwQWjcNHDs2XbL3hATQY+9XuTMXXxENX2/iBjzGTwjcKR5K7lnTkcXIEDeacN17x40TUTkoUljyYYjoVzupqkXBRPfTc6MUo9KVrp45FWP3o+PwzJzqp9hEgf0qcN/MhGRfKafrIGzT9fL5KJv2EQYjw6/rwDwd2daHx0FdkjleEFZfA/ApZjMem4QSiokIucklLFh8YlNWadVp2+66ftTBlkYBA58ZBJ7jKCcqQJwbX+IntOBcq/P5OoryKFJVcMmTln0LOdCo6QoBtDUNXk4poULEW8CgA4SthPS8ul5ekN8H0tKUNA8swot6XJGuV9Ed19Ajvak/8ONw1M6np/oEzN7/w7obxExpjJ4H8cVgd9XJq5jGzM5JUaC5lLkTFmMjjiyF1Shcofz4N9KCFLRxJmYjVegoEAwzJgbPRXQqtgpXNyMbXNKLSMpWQfNLg+bI6G94gyaQa2tpMbCIi0EhQ5+6AFY1YMiwbPuwOvpNwvKJ6XcEMq9V2I8Gh+aj06/7n7SPcqgwyOKJSemWFT9cfK02+fYvo6GWMmg6MCkocBEA2GwsvGouDC0UmPw5FE3hlyUlrRtZJU8gEmJ2uVC0iAJiIIAKxmdXG5AdH9QS26PzpktGsACHwaAF3/VtRWMnKLdGwZrzS0pQWJF+hoKmtEWnrhW9OkMtJUeh+SOlW9G1oQbfvmcn0yyCCdsEsK+Q5WZD2xhAUaaZseBsyYefzxx3HMMcfA6XQiLy+PuE9dXR2WLVsGp9OJkpIS3HvvvYhG5US5zz//HNOmTYPdbseIESPw0ksvDVSXM/iGIGt8EZxT6LWNBhSKd8hWkU38t1mU3jENBZfohKKMEGP30lOXtaB0oPR8dhg9n9T16VwJhOt7EDSjpRNHcJ/2d9ATG0zUipLhKAqFtfxyIzz/3q+qM6WVtfW/Uqoh2h1C9/u1iHq+eZylDL45YKSDm+Jdvf6ZY7H4anMJFiQMmDETDodx3nnn4aabbiK2x2IxLFu2DOFwGCtXrsRf/vIXvPTSS3j44YeT+9TU1GDZsmU47rjjsHnzZtxxxx249tpr8cEHHwxUtzPIQAZWIt9vVMeFBGuZC/ZhecQ2mpcKUIeeWA3vDS01O2tiUdpKA0hDTaTUayPlICL9VBqOxSdOafmH9he3E42kaGcQbS9s67MRaAahmm7ZZ5In638NHX/ZgZ7PD6P9xf/975pB3zH//JHJf+cr+DCclcWoWWWybU6tkDkFA2bM/OhHP8Kdd96JiRMnEts//PBD7Ny5Ey+//DKmTJmCpUuX4sc//jF+97vfIRwWhbGee+45VFdX46mnnsLYsWNx66234txzz8Uvf/nLgep2Bt91SFYN+eeMVDT2zyCgOQ/KH5qT2EPVln/GcNlnUjZTArlLyWS63KXVyD6m0lAf9cBT1IMTCO7sAB+MahfZ7K9dxZBrQLX/abtqW9db+xHa70H7i+q2dEOI6WeThQ52U9tIIoPfdEQaReJ1tDUTMsuAjtGzy7Ds5kk4/vKxqCSUtFFCi59Iw1HjzKxatQoTJ05EaWlqcF6yZAm8Xi927NiR3OfEE0+UHbdkyRKsWrVK89yhUAher1f2l0EGZuGaKV8tgDJX2apzDZ2PsZEJzWxiu+L9zTttGBhF5VlLobzkglWSBknizHAFDlgKHGkhKAOA57+i9otWGrgQ5RGuoU/aNM0Vo4i2+uH90BjfJ9KQMhBoZRzSBSNkav/mVmqb92N12C/c4IN/C/2YDDL4tqBqUhHGHlNObV98jRhqWnoj2QGih6NWNbu5uVlmyABIfm5ubtbcx+v1IhAIICuLnFL7xBNP4Ec/+tEA9DqD/3WwFIMDADVUU3ydsZdPi7xLgp1Qk8Q+xI2804eLqdYsQ84Ik11U/J+lwHjdqWhnkLp/JJ6Ro1l3SgD4AN1gidT3L8ykpUHDh2Oy35DvTfWj85U9KL62bwOlUfD+iCw0qUTv2mbknz2SeH9IRl7rb8R6WVyOHfZhxozmDNID3h8B47D0yUuQgXmMmlmGUcoFpAmYGl3vu+8+MAyj+bd799FPNbz//vvR3d2d/Dt82GB2RAbfeTjGFiBrcjFyCfoHHKUQZXKws/Rv0FNqwbBZ5LVG9jEVyBpXiKwxBdR9EugLaVlW7JGAaFdQM6TiW9WIjr8RNFUoKLhgtOF99dD1Br1mlpLT0l+EarvR/MsNsm1GqUk9n6nHJGkFc1XbtzAE9W1GuNGHxsdWo+Ov2u9CBt8cmPLM3H333bjyyis19xk2jCCERkBZWRnWrl0r29bS0pJsS/w/sU26j9vtpnplAMBut8NuN1YbKIMMpGBYBoUUATyGZWApylKJoSWQs2AQcZLSAitRFnbNKEXoYDcCW9oAFuBy+/8M552pX/+EdVrA+1NegUiztgCdb3UTrCV0UaueT83dAy4/fe9qYEsbEP/9fGub+n2+mDeMUG03ssYXqTR+2l/cDiEsN+oSvKh0aOkIklTub2KBzfaXdlDbBF5A15v7YCt3wTW34lvn3ehdLT47wd2dR7knGRiFKWOmuLgYxcXpib3PnTsXjz/+OFpbW1FSIqbhfvTRR3C73Rg3blxyn+XLl8uO++ijjzB37ty09CGDDMzCWu6iGzPHDkLvmiaZYQDocGokcx7DsSi8aAyEC0erNUr6CCPaNbahbgR3SQZtQT6RDjS4PO0QWOkd09DyzEZT54z1hOF5U6GBQ/hKsd4IQvs9yBpfSAwDJgpbMnYOlT86Rn66MN075f3YnIZP8pyCkPztfatS6su+rxrgPnGIjpbQkUO0K6g50Qf3dsG/vgV+AD0rGlDyvamGnsVvDCTPStQTIhbOjXYFEe0MwkEIB3/TEe0MouuNvWCsHPJOH64KK8e8YXg/q0P2nHJ5IeBvMAaMAFxXV4fNmzejrq4OsVgMmzdvxubNm+HzifHyk046CePGjcNll12GLVu24IMPPsCDDz6IW265JelVufHGG3Hw4EF8//vfx+7du/Hss8/itddew5133jlQ3c4gA21o2Bisw4KKh9WGdvZsjTgwIS6RLkOmP/CtbKS3fVGftlRvALDk2VF41Xhqu7XM/GDa/mdj2Usdf92Jzn/uRrcOoVgwWgWdYRBtD2h6p4hKyglIjMhuhcKx5nEDACHGw/tpHUK13arstFiPtqdIkHCCYp5Qn/SIjiak39fz1n7iPs3/tw7tz29DqO7bl2DS8bedCB3oRnB3J1qf26Jq73xtD3pXNaHlV5uOQu/6hgEzZh5++GFMnToVjzzyCHw+H6ZOnYqpU6di/fr1AACO4/Duu++C4zjMnTsXl156KS6//HI89thjyXNUV1fjv//9Lz766CNMnjwZTz31FF544QUsWbJkoLqdQQbaMGlouBcPRZYkk0g5Mbtm09n96UbuacZCwAAQ3KPtXu96nc5N6QscI/I021mnOY9EImVYBkLoJ3xInIjSVuVcEND5xl7NXVqe3kBta3jwa41z97VTfYNvdRO8Hx5C23Nb0fWa9ncCgIjEY6kMs/WuV1d0D2xvR/2DX6H+vhXwb2vrf4fTCUn3Qwc8mru2PbvlW1f9PNKSej94QggzydFKU+mRI4EBM2ZeeuklCIKg+lu0aFFyn6FDh2L58uXw+/1oa2vDL37xC1gs8kFr0aJF2LRpE0KhEA4cOKDL2ckgg4GEXlFKJdwnDJF5Wkq+N0VWlNJ9/JC09U0PtsE5xO2k0EVovydt17UUOlB09QRiW1a8eq6ekUgsSnqE4VtN91Yl4P3sMKL99KDQNHp4HW9IuhFtSX0P/yZ5ejgp9V5WSFMxCYbr1ATmjpd3AVEhfuzRTxyRwuzz79/8DTPGIBqU1HCxju0lBFOeyGjHt0NDKFObKYMMTCDn2EGwFBszaFiX2khgOFa2Xatw5JFC7qnDUP7A7AE7v2NsIewj84htCZIzwzIyD5YSzmlHvryFtDAkAHjeOpAUvaMpCvtWNMjSwfsEymq45+uG/p3XLDQW5aQwZFSiyKwpmvgtQExSikKI8KrvI0TkYUch+s0qTyEIAlp+uQFN/7dWZdCYNU40ZRi+QcgYMxlkYAKsjUPp7dOSn/PO0sgWonobjr4BIwXnsoJz96+op5YysfvEIVQekH14ihxNyiJLpMgPBI9IL0MoofEiRWLCNqMobKvS0QJSgA9E0fVvdRhPKaAIiAKF/RUD7H6/Bm3Pb5Wl2wtRHkFKeEXgBcS61dcUQrHUpE9Y+YcOks/3TUOEQPAPKYzXrrcOyHf4ptluMQHRtgB4bxjRrqCsKbBdW9xRacSTMvMEQUCkzZ+WrL10IWPMZJCBWUgmVrvWREWbwKvcsBRnwTG2IN090yxmybkHTq7ANoiuZ6OVgeMYV6h53pwFg/rcJz0oB2I9UisgCqmZRZEGuZmEpp+sQe8aNceEdI9bntmIpp+s6VcooOfzepEMKslo87x9QFb7CpB4ozQmsESGU0wxgQKAZ3mNqX7x4Rh6N7aQC4sOIFp/qzZiE8rXCfg3yCVDlOuTSKsf7X/Zocs9OyJQeskI45L0Xej8h0IjiuBl613ThJanNqDrNbqA5ZFGxpjJIAOzMOgkYChvF2NhUXrndBRe3v9KsUo4JxYnlWKVhoIlz46cRYNNn9MsT8gMlB6Xvhh42QuM152SquwqK1c3Pb4Gnv8eVB4iQ7fJCRkAGDY9wywpQyohExDYbj4UIAgC2l5IFYiUTmi969TGFMlbpITnP+L96/mCQKiWzIlGCLPd/z2Irtf2ov2FgSli2fXmPtTft0IltSDliySgx4NSile2PL0BwV2daP/zjj4ZwOmEqh4YwZiRcoSUtkuYoNjtjT+L/s1tECLfDPJzxpjJIAOTYFgGjtH5sA3OgaVYLR5nj2fmuObQM5UYlhmwFOzCS8ci/5yRKDh/lKrNvVhOODYSAtELQbm0Us9NwjkpxZthbPLhieZ1ylOQg7XqH3X8QySaRj0hYlaRb0UDhEgsvROQjjK02UwtErrfM29kxTqC5oiuSUVA+i58T5ju4ZLMkkRjRwF/vNZVpIku4tjXiTR00IPetaLB1vbHrX06h+x8GurNMd9RMGYkv5Hn34rUcsLjKOUIqTxyJM6M5Pzez9Q1xY4GMsZMBhn0AUVXTUDxzZOJyqaFl49D8fUTkXOseS9IOsA6rXDNLCOGdxhO/so7p6aItVZKqMhSSlf7BQDwdLVirbAXCY7xKW+Se/FQWRtJ1E65D6BduynBfQhspWef8KEYut5MX+q5ntHaV/K1Ua5MrDdCJOSqtugZ13HPjRZPQhBALwIqOSyws/+k0vDhHjQ89DU872p700ho+2PK2xPzhqmEbqPoXaWhNn0UeCV8iE5CJ41ZiTIgUYlRo3l+icEaOpDeMiF9RcaYySCDPoI2SbE2DvZhed86CXcauBwbKh+fT20XYjw1PGQleK60ICv0qZwDCJNCn7OcdH6a0KEjJ4RmpgCptL6UUhWZZLD4t7Wj6cer0XD/VyojRHkLko4XSt2tmEec6PwEzRgpaBNppKlXswAprxAm1LOtuj+oBSCqI/cXCUI3H6ZnJek9E9FONU8IoGe+DSSaf7Ge3qhxX5ufWEtvpEAZrj1ayBgzGWTwHQMjie8zUuOBYCwwVhbZ8ys1U8izxhVSDTtLYUomPXtehbl+2uXZO/YR+dR9HaPpbZSz05sEaIZSlGm5Wkj0K12p5W1/EEMiUU8QvDJ8ofj9Wp/bItN+UYaUYpRQWmCrdrZLghdDRJTXXKknwmEkReWeT49+uEJLubnt96JSLi0Lrv1PordHaTR2L68hetH829r67aHi/RGiN0XJ+5EZun1YYzU/RTeOtMKARxIZYyaDDL5jKLx0bPLfzklFqQbCgrzsBzPl3hIF8i8YLQsNKSFNJ849dRjK7p8l+0xC3pkj4BhfCJci3Zu1E/oRH6PZHOOp5bw/ojugqwwFCRoeWmn4WnmnDQdAJ1EX3zTZ8Lmk4HvU/VPqiSircCv5Jd73a+UniN8TLe+EFh8p2TeN0g+9G1oQ2NlBrG+mTCEWJN1Np9ZJ7/oWapuR6uQ0TlC0Q+y/72u1Bo9SNDDmC6Pz77vR8ded/UpvbnxsNZqfWKub8ZXwqgHa2YU0RNsCSY/dkUzHDh7wIHTIWBgrY8xkkMF3DI7heSi4aDRKbpsq49AQhc50xi3n5GLDRGaGYWCRcGsYCjE2e045ii4bZyz80oeB1ft5PXgtvkkax+rkwE/pp32oOQ2aBEgGR9frOiUHFL9vWFXyQf931OIjJaGVqRQT0PHXneQ2yXMkRGIQJJlnHX+jHNMH0O6T9+NDhrwWMQ1eSXC/B2FSrSbF7y8Lt6XheZNWuu9Vpo0D8Lwj0cWhXE/XSIn/rE1PGgtF9dfo4QNRtD+/De0v0quzS5ExZjLI4DsI5+QS2CoUhF/S4KM3HvWHFpSGQZyxikOYGX5S6IAHPZ/Ts2lIdYT6DA3SrC6xmoJoZxDtz6vTlfXCQ1JbJtoRkBkLgGgwRD1k3seRgNRzpJUdZOxcMXS/Xwv/1jZ0vbWfKISnhPfjOoT2ebTPK2gYYwDaX9gGNltdHVz1+wvUD32CdD3R9S+1sRbc1YmYN0TuSwI6xkfiOFItJyU6/rkbLU+tNxWSVYJoFGogY8xkkEEGAIDsY9ScFjZHMjATeDNSr0zOcQOfvSVNd885YUgqi0rDllFydSINat0MKajZODRojKLJ0hckPpJB+4srcMg+N/9sne4xKm0RyAX/SKEQgJDGq4OSW6eY2l8LQSl/RM9JEI5pppV7PzuMns8Po/Mfu9G7uinJNeovjKSCJ3SepFD1Vfo89NGWkXtS9R+mpp+uReiQl2jsxE+od0HDZSoCW9oQ7Qii6efrTZe2iPVGENgtavSYQcaYySCDDACQdXGkxor7RHUatBSsS70i1QKt8KUW8s4YjorHjsGgJxcgV5KW7ZxGL6eAAc4qY6wcim8mc18SYTzialhyb5WaOlK4TRiJXW/uQ6S5F20vqj033RJBQFJtJYBsBGnBWu5CiaS8R7pAypAJSVbqWqn1gJi2LUW6inQaydzpJhCke9c2ywQbe6QZWH00ZmTf0eAjniAxk0AtShlH93s1suKjUij5Tgnw3jDVcKah9deb0PGSOUMGyBgzGWSQQRwMw4DLp5c8yJlfgaLrJlLbtUoaSFH2/Zkoum4ibIPMGzMMwxAJyfahbhTfMIl8zEAX8xQA+xBt7outkvBdJd2iZWqV/3B2qrK4AfSubUbLrzcChIlJCOt7FYQIb3JyZWArd5k5wBA6Xt6l2hZtlYSKlCq1Cm+bXrioryARl5WgZTvxkgwjeTkE89ZM+HAP2p6VGCZpeMQbH12l2d67ppnqmZKKICoztLpN6gBpcZK0kDFmMsgggxQ0Yh+MlYNjeF7ys9LwsVflovAK/RINlgKH7DzpgjIck8QAKS0nT2/VH0azpFljCRjxGAlxEUQNNWkVePSLhsEHTVT91vkKpXdOo2at0UDn7dDTi0m1oIhnMFBGQQttz/UnXEUOLZktMB484EHr7zbLNw7wM5689j6yZo7UANHiFCXg/fww6u9bga639yPaHUpLlfWMMZNBBhkQkbNIu8gjKdsoa2yh6SrRAw1l3Zx0w4gBxzCMSiVZRlqmzEWMXbzHzsnF5B1MQunBIMH7sQm9FwNzqGYxVgWESAzd79VS2njivwG6YJ0SWqJ9fQFXSDGgSaDN12aNmaNYvJLGJ5MWKTV0nrgsQO+qJjQ/sRbeD0zy1AjIGDMZZJABEbknVxO3c3nipJylU/H6SIOU0eQYW4DsudpeDdfMvteW4nJtuiGmBFSrT+lqmjChFVw4GqxdNMTSpe0Rae7VXwWb8F7opeXTylzQ0PDQSgS2UPgwGt3uXl4j49QQD4/E0s6fKrtnhuF9Yx7RA6H0PDU+stKQvk0ShPtgJPw10OiPd6Xnc7pYoVFkjJkMMsjAFEpunoL880bRCcFHvhQNALHsgtJTUHTFeJlwnxK26lzknTWC2l5yyxTNa7rmGlc1LrxYUadKMvqS1JGdUySqwWkyZrr+tRf+DfrCd+kC67CkzRDTO49/k/b3ant+W9qjMWaKxXa9tR/ej+vQ/KQ6G631t5uT/+ZDMfSubwHvj6Drrf2ov2+F3KNGuA3UDKV+ovDK8Yb3jTT1IlR79Oo0ZYyZDDLIwBQ4tw2u6aV0rkga4t99hbSGlJEQQOGFozVDJbbBOcSU9QQcI1PEXT1ei71KnrJrk3h0HMPzZEU/VTyTNKquBrZpZwKlG0Tyc18gea4SdZmk0DMslCq8RxoxTwg9n+iH8Dpe3omu1/ei8bHV6F0tFrBs/c2m/2/vzuObqPP/gb8mR5MmbZIeaVOgB6VQwFKgFEq55C4KSLGCICoFyv7AgpwiiJyKrrgs4ipeQKtyFHSR5dRlWcCraGFpERVYdsHKlgLfCq1cLcf8/igJmWQmmaRp0mnez8ejj0eTOfL5ZJrMu5/rXd/Fs6PpFInA1vw51/iwNXdw2cWp/Z5EwQwhxKN8GMtwupqCe94f8yM0OFiuV4FhGIe5nXQDhaekBzS9P4MruNf911OatDBO4p9dZTlvvxjOY5nufkqG4B5NOds8uYS8q9cndFSiW69jfk89NpvMqvfLNvcQANSUOR8P5Grd5WFqhzP4XMGXj4qPuzOxxKyB4xJ3LpvA3+nlbfUf5FAwQwjxLF9GM1bkVvmawp0M0nXUnC5TKxCS1dLp6ymsAqagHk3tWmIAQJtWOz5H28VkN8XcPAWbbwVZZaTnpj9Xu5DFOaC5jtvd5QKl7QrTdXTjR8crHNvmouJznWepf0eM45PqZeZdnQh8vq6XXMLtCs+NnXGlCw2ozU11+xL/65tbmADhwdp1Ddjrd5g/IcT/+DiWCXm0JWrO/Q51m/tN5IoIx6kDnH1xazubwChl4nITARB6EwyPtICmYwTvgoGqGB0iZ6RYBlhbUwi0LAUmh+P2xRuc3DyeZBjSQnijkxYXVxdRdEZsN5GjsTOVu89wHgd1byK4qJumYwQUYfwJQq3JdQG8a8sYHk3Ala310CIhlFvpzl3PJoEU0dQhN6gsSSzFjtupEupqu8PWaYA2tcwQQizMXR+aFPf+GwcA/aA4APyDWr1B28WEkEdbcrqcXP0vk4+oxJf3CLVKMHIZVHF6ToJPznGRWssMJrttTbnn1D8Uh9CRiQh53L1uIDHM3WhNlqTbbbPtCrMVlHZ/DJH+If6Zce6wTsvA57fNYgNO/tQDFk5urOagM/wP/N2JCoML07bvEbMOztVC4RV1rbNjOxO1oCuiXugiuF0WKCIYdSN2EmodM2fldhe1zBBCLLSdIqGK17s8pdaaumUImixOh0zdsL9eAl1cu0Wm4dZHP9h+MTjTc6m4faXaPomnB4SOSsSFFUcsj5UmLRiFrM4r8KoTQ3DzpOOuJ5lKgYA43f2uHAUD3cA4h8dYDxAPfrAZKveccbC3eNarzdaVurXw8gKqBIPDYyOe6QAAUIbzt964OlX56rdluO0gGLlbfbs20BU4bU3p73a5tUJHt8Zvm07w7i930nLmbJ2pWh4cy+UknYIz1DJDCOFQhKhdykLNpyEGMratA9azh8QIaK7nDCQO7mnfMqEIC6y3MRZKI7erzPpeqXaw5o+jFBUAED4uSdTrq5rfb8UwTkzmDOw1/qGd05u/pzgKZu5Wu7gonoM/c00Hx8Gu3GrANh9X1365sv0/uH5UeExP5a7aYFAm8Lp8LR7KJu4FuoxSJuoz7NHhcXVsmaFghhDiF4JsukVcDdgYhrF0oflKk8VW3T3W/8k6qoqHbjjWLS22XW6qeAPCnmxjeax0I4moGNd/cDwI2HzDF42B4Ngf665J/VDXUjIAEOxKdNfNU5fB3mVx95rjbjZrtgEwH74uZcMjDsZKWXMhmqkurRIeLwOAvU0tM4QQ4pTdFGF3vv18PLhZplZAnRgCWXAAVC0NlucVdegWBIDAdjy5o2wEOVlHR6ZWILhfDBi1vHb9HhvWA7LNjH9wbdrzbxvsE1Bau/Z9uehzBfVsCoZh0GSR/Xggu33TXMiNZSYDIqZ2dPEg4aj0zpVqXFjFn0SUj+LeOkumOZ0d7qduZb8sgdpqfZngvsJZ210J2C6tLkHVXuG0BXUdM0PBDCHEL8mDHHcT8PH0DB13hGU/gKi5XThTu3UDBNbCkTMITHIQqNwL8PgCDVuM9cBkgXuufkAsmi7uxjsLyLZlTD+4OVTxBqevWxcBMcItRLq+tYPdZQFyh6tAA7UtUa4GJsrwQM46RGKwNY7Xorl94broc0XeS7UgNBPOTN2G20Vpei6Vs6yBo78fvmUE3HaHFbVWkGBZPFcSQgiRDqVJeDxBQKwO4ePs155RtdAjuHc0QutxBpEzDMPYtTIJJdM0TkiCPiNO8FyaZKP5pCJe2OpXN8ZU2ZbRUZoJC0Xdxm7xDdLmK4+2U6TTc7k6/kTZ7F4g5cJdVuzCemKIncEnU3Gvg5jp6AAg0yqgivVcUtmq/b/i4upiu+eD+8fY78xXHo+VhBBCGjjzdHFni+BFTG4PdaJ9a4V53Iyrg4d9RRVvAKOU2U3rNjMv4ieK9b3RjVV97WZ4OTmFTKtA5LQUhyswOyNTycUNTBYTyzkKDng2mQM+40THK0F7g20y1eA+wl1HdnjqrekYAeP/aw+dg0DZVTdKLgE242b0DzeHrqeYWVUUzBBC/Ih+SDyi5nWpU6ZsqbDuAhDKo6VwMEDU9j1iGAZBPZpC0ykSCoHpyM4EWP0nbw5uQkclQhZs3+UXOroNlEaNwzKKoe3svNWFkcscdkmZKe9Ng7edph85PcX+Pb4XBATEea71wpbobh6botkGiCFZLSHTKBA5I8XuUL4YLvTxRCgjNHarWHuSKsGAIBcSuVIwQwjxGwzD1GkNHSkxWi3mxvDcdIL7RlvWGhHbzmIYEo/QEa08sgiheRVkTYcINJmfZr/DvZdw9lKOBqgCED1oW9vlfvCmbMbfkhX2VFtou5hgnNSe87wyUovQUVZZ0eWM5e4q9F4p67g+EAAE9xbXwqJJ4QZ0tmXSdjahycJ0j6bNcNSNK0bYE62Fk9nyqLdgZtmyZejWrRs0Gg0MBgPvPgzD2P0UFBRw9jlw4ABSUlKgUqmQkJCA/Pz8+ioyIcSP6e71zTvKki0l1gM/ZQH2X/XaVB+0TrkSA4kMQpwNyhY7e9g6jYRhKP/UZEWoGiGPtoSSJz2GLPh+OZq+1N1pwBc+LklUZndHhMYuKYzclrM6jW1xM251ddFA++Nd27/egpmamhqMGDECkydPdrhfXl4ezp8/b/nJzMy0bDtz5gwGDx6MPn36oLi4GNOnT0dOTg6++OKL+io2IcRPBfeLQeTMTtAPcX1NkYaOb7Vjxmrgp92YGgUjcgVY1xiGxAMyB7OvrFgG6Dq5mTq914rMV6RqYYB+cDzCxrZ16+avitFBNyAWoaMTRQ2QZgJkMI4Xt2ChIIE7vm3LUV04WzzP9Dz/1G9Vc73Drruwp9oIbnNHvS3TuWTJEgBw2pJiMBhgMvH/h/Duu++iefPmWLFiBQCgTZs2+Prrr7Fy5UpkZGQInrO6uhrV1feXha6qcp5NlRDi3xiG4f2PW4pUrUI4C9vxTYG2vkkpIzTQpEbi+uHaVWSbLuluvy6PBwQ0C0bTl3qIOrfYac2OQhVZcIDoFh6GYXhXdXaFObeZyBcUN4vMAb4EqrpBcQ5TFbi6vIBcr4JheIJdqgRLGULsW5d0/WMQ1LMZrh06L5ggVOVkpWyhGXqC+7u0dz3Izc1FeHg4unTpgnXr1nGapgoLC9G/f3/O/hkZGSgsLHR4zldffRV6vd7yEx3twshtQgiRIOsxGLb/8fPeM22+/a3/i66PQMaVc1sPmg1oJvzfPaNWCK6jok0z1d7UPbrmvufYTom2ZZzkfBaUKsEAwzBul5izdBrKSNcD9qC0KGjTaxcO1IlYBVvXPxYyldyuu8uaTK1wuFK0q9P/fRrMLF26FFu2bMHevXuRlZWFZ555Bn/5y18s28vLyxEZyR24FBkZiaqqKty4IZz3Yt68eaisrLT8/Prrr/VWB0IIaQgc5QpieP7LtR3TUdd8XB5lFX/I9SreAbnarlGImpPKWa3WmipOb3cus9DRvlsniEMg0FIYA53O4tIPja+dYebCjB8Abo+BMQxtgchZnRD8oPjuR+t8XnwUTvKGucKlYGbu3Lm8g3atf06c4M/QyWfBggXo3r07OnbsiOeffx5z5szB66+/7nIlbKlUKuh0Os4PIYQ0ZobMBATE6hA6xn4sAsMwYNSOWwLULWuXtZfrXV8Z2RM4AZfNTZ6v9UXdKgQyjVJwoK25C8Z6nRlVSwOiXugCTXvvrhMU9QLPbC0ArNB4HhkDuVaJkBGtBM8p1MIilFjUnN4gMNm1bPFmjIyB0qhxPrA5536KCsG/OXPg7MFGM5c6pWbNmoXs7GyH+8THuz94Li0tDS+99BKqq6uhUqlgMplw4QI3E+iFCxeg0+kQGOjeOgeEENIYKULUiJgsPPAzckpHlP/psOB2uV6FqBfTIFP5JuN55NSOKF9eBMBx15KZOtE+p5CZwng/lYAiVA3TvC6QBSrqdV0UR2xbzSwDsgWCGfN4J22nSFz+5BT/SQVSGQml6YjI7YCa/111OlalrtRWwaNg4GMJZjwXzbj0V2s0GmE0uhfViVFcXIyQkBCoVLWRZXp6Onbv3s3ZZ+/evUhPd54YjBBCyH2K8EBo00y49p1wMkZ38lV5iiJUjcjpKbj+w/8huBe3K0ObFoUbx+5nzGZUcodJDgPbcvMN1TURp6eF3BvnIrTmkW1Wcj58rTp8s9bMZBqlpfXN01TxelT/t5J3G6OS26VpsIyb8mDLTL2NmSktLUVxcTFKS0tx584dFBcXo7i4GFev1iaS2rFjB9asWYPjx4/j9OnTeOedd/DKK69g6tSplnNMmjQJ//3vfzFnzhycOHECq1evxpYtWzBjxoz6KjYhhBAfUZq00A+ItRscq25h4GT2brKgq7eL5jH6IfGQaWpnFMnUCkTO6mS/k3UeLIGuGr6Zd76ajafiybxtZnou1f7Jey0znhybXW/tiQsXLsSHH35oedyxY23G0f3796N3795QKpV4++23MWPGDLAsi4SEBPz5z3/GxIkTLcc0b94cu3btwowZM7Bq1So0a9YMa9ascTgtmxBCSOMTOrIVbnaMgLqlwWnLhVAuqobAdjaXkmegr/XU6qg5nVG29BBne/jEdpxxRCFZLXHj+P/ZZSb3luAeTcFW3+EdjM3X2mcebK7pYMTNnyrstzuZ6cWn3oKZ/Px8h2vMDBo0CIMGDXJ6nt69e+Po0aMeLBkhhBCpYZRyu+4jM3mYGncqbloeW7fiNBSalAjcPH2FN0mptosJ176/3/1nnX7C3IpjTWWzGJ22s8mn+cYYhcxhdna7/e+lKeC7TprUSNFpGqz5fJ0ZQggh3sH4aHBvfYucxk2Q6IncUZ4WOjIRUXO78K+oa1Nc20SeqnibKc4NsH4uude9xHedQh9rBaUbiUwpmCGEED+h690MAXE6GIYn+LooHuWrWUquElzLx+qmrm4diuBe3O6i8IntBPeXIk2K56fGUzBDCCF+QqZRImJSewSlRfm6KMSaVWxiyEwAo+QGZwzDcAMaqd25FfcrGPZ0W07ah7AnPZOjSWpvCSGEECJI4cZy/b4W3PP+VHShRQutu14aYjeaI9bBWWDbMM4AbvUDYTBk8mcpd0Xj7EAlhBDil9zJeO1rilA1mr7UHVAwgoGKXK9C6BOtnWaxboi0HSNw9dsy3lxMDMMgqGsTyEPUdaqb9N4VQgghxEb4+CRcO3IBehGJEBsi8wwfRzRupiLwNf1DzREQp+OsDmwrMJE/x5ZYFMwQQgiRPHWrEKgdLN5GfIdRyuo9EKMxM4QQQgiRNApmCCGEECJpFMwQQgghRNIomCGEEEKIpFEwQwghhBBJo2CGEEIIIZJGwQwhhBBCJI2CGUIIIYRIGgUzhBBCCJE0CmYIIYQQImkUzBBCCCFE0iiYIYQQQoikUTBDCCGEEEmjYIYQQgghkqbwdQG8gWVZAEBVVZWPS0IIIYQQscz3bfN9XIhfBDMVFRUAgOjoaB+XhBBCCCGuqqiogF6vF9zuF8FMaGgoAKC0tNTuzejcuTOKiopcPqc7x3nztaqqqhAdHY1ff/0VOp2u3l/P28dJpX6efC0xdZbCtRN7HF99G2I5Pfla+/btc/nvWgrvidBxjeVv2pVjrOvcr1+/Bl83Tx3nyne29XGVlZWIiYmx3MeF+EUwI5PVDg3S6/V2b6JcLnf5Zujucd58LTOdTtfgy9mY61cfr+WozlK5dq4cZ13fhlxOT76WK3/XUnhPnB0n9b9pd47R6XSSqJunjxPzt813nPk+LsTvBwDn5uZ67ThvvlZdeLucjbl+Unkv6TjPHSeFMtJxnj1OCmVs7McxrLNRNY1AVVUV9Ho9Kisr3W4BkJrGXufGXj8+/lZnf6sv4H919rf6Av5ZZ8D9eos9zi9aZlQqFRYtWgSVSuXronhNY69zY68fH3+rs7/VF/C/OvtbfQH/rDPgfr3FHucXLTOEEEIIabz8omWGEEIIIY0XBTOEEEIIkTQKZgghhBAiaRTM+BGGYbBt2zZfF4MQQhyi7yriKskHM9nZ2cjMzPR1MbwmOzsbDMPY/Zw+fdrXRaszc90mTZpkty03NxcMwyA7O9v7BfOSwsJCyOVyDB482NdFqRf+fn0B//u+MvOXejf2zzCfS5cuYfLkyYiJiYFKpYLJZEJGRga++eYbr5ZD8sGMPxo0aBDOnz/P+WnevLmvi+UR0dHRKCgowI0bNyzP3bx5Exs3bkRMTEydzn3r1q26Fq9erV27FlOnTsWXX36JsrKyOp3rzp07uHv3rodK5jn1eX0J8TVPfoalIisrC0ePHsWHH36IU6dOYfv27ejdu7clJ6K3NKpg5vPPP0ePHj1gMBgQFhaGIUOG4D//+Y9l+9mzZ8EwDLZu3Yo+ffpAo9Ggffv2KCws9GGpXWeOfq1/5HI5/va3vyElJQVqtRrx8fFYsmQJbt++zTn2/PnzeOihhxAYGIj4+Hh8+umnPqoFv5SUFERHR2Pr1q2W57Zu3YqYmBh07NjR8pzYa71582Y8+OCDUKvV2LBhg1fr4oqrV69i8+bNmDx5MgYPHoz8/HzLtgMHDoBhGOzatQvJyclQq9Xo2rUrjh8/btknPz8fBoMB27dvR9u2baFSqVBaWuqDmjjmqevbt29fTJkyhXPuS5cuISAgAPv27av/inhAXFwc3njjDc5zHTp0wOLFiy2PGYbBmjVrMHz4cGg0GrRs2RLbt2/3bkE9TEy9pcjRZ9j8+bS2bds2MAzDee7ll19GREQEgoODkZOTg7lz56JDhw71X3g3XblyBV999RVee+019OnTB7GxsejSpQvmzZuHRx55xLJPTk4OjEYjdDod+vbti5KSEss5Fi9ejA4dOuC9995DdHQ0NBoNRo4cicrKSpfK0qiCmWvXrmHmzJk4fPgw9u3bB5lMhuHDh9v9hzp//nzMnj0bxcXFaNWqFUaPHm1305ear776Ck8//TSmTZuGn376Ce+99x7y8/OxbNkyzn4LFixAVlYWSkpKMGbMGIwaNQo///yzj0rNb/z48cjLy7M8XrduHcaNG8fZR+y1njt3LqZNm4aff/4ZGRkZXim/O7Zs2YLWrVsjMTERTz75JNatW2eX8v65557DihUrUFRUBKPRiKFDh3Jam65fv47XXnsNa9aswY8//oiIiAhvV0MUT1zfnJwcbNy4EdXV1ZZj1q9fj6ZNm6Jv377eqYiXLFmyBCNHjsSxY8fw8MMPY8yYMfjtt998XSxiQ8xn2JENGzZg2bJleO2113DkyBHExMTgnXfeqccS111QUBCCgoKwbds2zmfR2ogRI3Dx4kXs2bMHR44cQUpKCvr168f5Gz59+jS2bNmCHTt24PPPP8fRo0fxzDPPuFYYVuLGjh3LDhs2jHfbpUuXWADsDz/8wLIsy545c4YFwK5Zs8ayz48//sgCYH/++WdvFLfOxo4dy8rlclar1Vp+HnvsMbZfv37sK6+8wtn3448/ZqOioiyPAbCTJk3i7JOWlsZOnjzZK2V3xnwtL168yKpUKvbs2bPs2bNnWbVazV66dIkdNmwYO3bsWN5jha71G2+84cUauK9bt26Wst66dYsNDw9n9+/fz7Isy+7fv58FwBYUFFj2r6ioYAMDA9nNmzezLMuyeXl5LAC2uLjY62UXy5PX98aNG2xISIil/izLssnJyezixYu9URW3WX9fxcbGsitXruRsb9++Pbto0SLLYwDsiy++aHl89epVFgC7Z88eL5TWc9yp92effea18nmCo89wXl4eq9frOft/9tlnrPUtOC0tjc3NzeXs0717d7Z9+/b1Wew6+/TTT9mQkBBWrVaz3bp1Y+fNm8eWlJSwLMuyX331FavT6dibN29yjmnRogX73nvvsSzLsosWLWLlcjl77tw5y/Y9e/awMpmMPX/+vOhyNKqWmX//+98YPXo04uPjodPpEBcXBwB2ze3JycmW36OiogAAFy9e9Fo566pPnz4oLi62/Lz55psoKSnB0qVLLZFyUFAQJk6ciPPnz+P69euWY9PT0znnSk9Pb3AtM0aj0dJMm5eXh8GDByM8PJyzj9hrnZqa6q1iu+3kyZP4/vvvMXr0aACAQqHA448/jrVr13L2s752oaGhSExM5Fy7gIAAzt92Q+WJ66tWq/HUU09h3bp1AIB//etfOH78eKMcQGx9TbVaLXQ6naS+r/yB2M+ws3N06dKF85zt44YoKysLZWVl2L59OwYNGoQDBw4gJSUF+fn5KCkpwdWrVxEWFsa5N505c4bTbRwTE4OmTZtaHqenp+Pu3bs4efKk6HIoPForHxs6dChiY2PxwQcfoEmTJrh79y6SkpJQU1PD2U+pVFp+N/dZNsTBkkK0Wi0SEhI4z129ehVLlizBo48+are/Wq32VtE8Zvz48ZYxEW+//bbddrHXWqvVeqW8dbF27Vrcvn0bTZo0sTzHsixUKhXeeust0ecJDAy064NvqDxxfXNyctChQwecO3cOeXl56Nu3L2JjY71Wh7qSyWR23RB8g9Stv6+A2u8sKX1f2RJbbylx9hlujHW2plarMWDAAAwYMAALFixATk4OFi1ahGeeeQZRUVE4cOCA3TG2Y4jqqtEEMxUVFTh58iQ++OAD9OzZEwDw9ddf+7hU3pOSkoKTJ0/aBTm2Dh06hKeffprz2HrgZUMxaNAg1NTUgGEYu7Eujela3759Gx999BFWrFiBgQMHcrZlZmZi06ZNaN26NYDaa2We8XP58mWcOnUKbdq08XqZPcET17ddu3ZITU3FBx98gI0bN7oU+DUERqMR58+ftzyuqqrCmTNnfFgi72hs9RbzGY6NjcXvv/+Oa9euWf7BKi4u5uybmJiIoqIizvdzUVFRvZe/PrRt2xbbtm1DSkoKysvLoVAoLK2rfEpLS1FWVmYJBg8dOgSZTIbExETRr9logpmQkBCEhYXh/fffR1RUFEpLSzF37lxfF8trFi5ciCFDhiAmJgaPPfYYZDIZSkpKcPz4cbz88suW/T755BOkpqaiR48e2LBhA77//nuXmkK9RS6XW7pQ5HI5Z1tjutY7d+7E5cuXMWHCBOj1es62rKwsrF27Fq+//joAYOnSpQgLC0NkZCTmz5+P8PBwya7d4anrm5OTgylTpkCr1WL48OH1Xm5P6tu3L/Lz8zF06FAYDAYsXLjQ7r1ojBpbvcV8hr/44gtoNBq88MILePbZZ/Hdd99xZjsBwNSpUzFx4kSkpqaiW7du2Lx5M44dO4b4+Hgv1sY1FRUVGDFiBMaPH4/k5GQEBwfj8OHDWL58OYYNG4b+/fsjPT0dmZmZWL58OVq1aoWysjLs2rULw4cPtwwDUKvVGDt2LP70pz+hqqoKzz77LEaOHAmTySS6LJIfM3P37l0oFArIZDIUFBTgyJEjSEpKwowZMyw3AX+QkZGBnTt34u9//zs6d+6Mrl27YuXKlXbN7kuWLEFBQQGSk5Px0UcfYdOmTWjbtq2PSu2YTqeDTqeze74xXeu1a9eif//+dl+CQO0X4eHDh3Hs2DEAwB//+EdMmzYNnTp1Qnl5OXbs2IGAgABvF9ljPHF9R48eDYVCgdGjR0uiO9X8fQUA8+bNw4MPPoghQ4Zg8ODByMzMRIsWLXxcwvrRmOst5jN87tw5rF+/Hrt370a7du2wadMmu6noY8aMwbx58zB79mykpKTgzJkzyM7ObtB/10FBQUhLS8PKlSvRq1cvJCUlYcGCBZg4cSLeeustMAyD3bt3o1evXhg3bhxatWqFUaNG4ZdffkFkZKTlPAkJCXj00Ufx8MMPY+DAgUhOTsbq1atdKgvD2nbkScygQYOQkJAguSZmQsQ6cOAA+vTpg8uXL3u8n1nqzp49ixYtWqCoqAgpKSm+Lo5T/vp95a/1rqsBAwbAZDLh448/9nVR6s3ixYuxbds2u243V0m2m+ny5cv45ptvcODAAd7l0QkhjdetW7dQUVGBF198EV27dm3wgYy/fl/5a73dcf36dbz77rvIyMiAXC7Hpk2b8I9//AN79+71ddEkQbLBzPjx41FUVIRZs2Zh2LBhvi4OIcSLvvnmG/Tp0wetWrVqcKtY8/HX7yt/rbc7zF0yy5Ytw82bN5GYmIi//vWv6N+/v6+LJgmS72YihBBCiH+T/ABgQgghhPg3CmYIIYQQImmSCGZeffVVdO7cGcHBwYiIiEBmZqbdMsc3b95Ebm6uZdnkrKwsXLhwgbPPs88+i06dOkGlUglmIj127Bh69uwJtVqN6OhoLF++vL6qRQghhBAPkEQwc/DgQeTm5uLQoUPYu3cvbt26hYEDB+LatWuWfWbMmIEdO3bgk08+wcGDB1FWVsa7tP/48ePx+OOP875OVVUVBg4ciNjYWBw5cgSvv/46Fi9ejPfff7/e6kYIIYSQupHkAOBLly4hIiICBw8eRK9evVBZWQmj0YiNGzfiscceAwCcOHECbdq0QWFhIbp27co5Xmhe+zvvvIP58+ejvLzcshjZ3LlzsW3bNpw4ccIrdSOEEEKIayTRMmOrsrISQG3mYAA4cuQIbt26xZnC1rp1a8TExKCwsFD0eQsLC9GrVy/OqqoZGRk4efIkLl++7KHSE0IIIcSTJBfM3L17F9OnT0f37t2RlJQEAJaWFNvVUSMjI1FeXi763OXl5Zwlls3nMG8jhBBCSMMjuUXzcnNzcfz4cclmSSaEEEKIZ0mqZWbKlCnYuXMn9u/fj2bNmlmeN5lMqKmpwZUrVzj7X7hwwaWsmyaTyW4GlPmxK+chhBBCiPdIIphhWRZTpkzBZ599hn/+859o3rw5Z3unTp2gVCqxb98+y3MnT55EaWkp0tPTRb9Oeno6vvzyS9y6dcvy3N69e5GYmIiQkJC6V4QQQgghHieJYCY3Nxfr16/Hxo0bERwcjPLycpSXl+PGjRsAAL1ejwkTJmDmzJnYv38/jhw5gnHjxiE9PZ0zk+n06dMoLi62HFtcXIzi4mLU1NQAAJ544gkEBARgwoQJ+PHHH7F582asWrUKM2fO9Em9CSGEEOKcJKZmMwzD+3xeXh6ys7MB1C6aN2vWLGzatAnV1dXIyMjA6tWrOd1DvXv3xsGDB+3Oc+bMGcTFxQGoXTQvNzcXRUVFCA8Px9SpU/H88897vE6EEEII8QxJBDOEEEIIIUIk0c1ECCGEECKEghlCCCGESBoFM4QQQgiRNApmCCGEECJpFMwQQgghRNIomCGEEEKIpFEwQwghhBBJo2CGEEIIIZJGwQwhhBBCJI2CGUKIpPTu3RvTp0/3dTEIIQ0IBTOEEEIIkTQKZgghkpGdnY2DBw9i1apVYBgGDMPg6NGjGDNmDIxGIwIDA9GyZUvk5eX5uqiEEC9S+LoAhBAi1qpVq3Dq1CkkJSVh6dKlAIAlS5bgp59+wp49exAeHo7Tp0/jxo0bPi4pIcSbKJghhEiGXq9HQEAANBoNTCYTAOB///sfOnbsiNTUVABAXFycD0tICPEF6mYihEja5MmTUVBQgA4dOmDOnDn49ttvfV0kQoiXUTBDCJG0hx56CL/88gtmzJiBsrIy9OvXD7Nnz/Z1sQghXkTBDCFEUgICAnDnzh3Oc0ajEWPHjsX69evxxhtv4P333/dR6QghvkBjZgghkhIXF4fvvvsOZ8+eRVBQEN5880106tQJDzzwAKqrq7Fz5060adPG18UkhHgRtcwQQiRl9uzZkMvlaNu2LYxGIwICAjBv3jwkJyejV69ekMvlKCgo8HUxCSFexLAsy/q6EIQQQggh7qKWGUIIIYRIGgUzhBBCCJE0CmYIIYQQImkUzBBCCCFE0iiYIYQQQoikUTBDCCGEEEmjYIYQQgghkkbBDCGEEEIkjYIZQgghhEgaBTOEEEIIkTQKZgghhBAiaf8fpfP3AyAyaHsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03bd0d06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T13:23:10.864951Z",
     "iopub.status.busy": "2024-04-02T13:23:10.864478Z",
     "iopub.status.idle": "2024-04-02T13:23:10.972982Z",
     "shell.execute_reply": "2024-04-02T13:23:10.971729Z"
    },
    "papermill": {
     "duration": 0.124079,
     "end_time": "2024-04-02T13:23:10.975725",
     "exception": false,
     "start_time": "2024-04-02T13:23:10.851646",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ts</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-08-31 23:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>104.739477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-31 23:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>68.398009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-31 23:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>188.454744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-31 23:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>-81.748381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-31 23:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>-87.107538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-31 23:00:00</th>\n",
       "      <td>5</td>\n",
       "      <td>30.775797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-31 23:00:00</th>\n",
       "      <td>6</td>\n",
       "      <td>-158.752245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-31 23:00:00</th>\n",
       "      <td>7</td>\n",
       "      <td>-49.775723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-31 23:00:00</th>\n",
       "      <td>8</td>\n",
       "      <td>-61.454939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-31 23:00:00</th>\n",
       "      <td>9</td>\n",
       "      <td>-4.737532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     unique_id           y\n",
       "ts                                        \n",
       "2010-08-31 23:00:00          0  104.739477\n",
       "2010-08-31 23:00:00          1   68.398009\n",
       "2010-08-31 23:00:00          2  188.454744\n",
       "2010-08-31 23:00:00          3  -81.748381\n",
       "2010-08-31 23:00:00          4  -87.107538\n",
       "2010-08-31 23:00:00          5   30.775797\n",
       "2010-08-31 23:00:00          6 -158.752245\n",
       "2010-08-31 23:00:00          7  -49.775723\n",
       "2010-08-31 23:00:00          8  -61.454939\n",
       "2010-08-31 23:00:00          9   -4.737532"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### TRAIN TEST SPLIT AND REARRANGE DATA ###\n",
    "\n",
    "train, test = train_test_split(df, test_size=24*7, shuffle=False)\n",
    "\n",
    "train = pd.melt(train, value_vars=df.columns, ignore_index=False,\n",
    "                var_name='unique_id', value_name='y')\n",
    "\n",
    "test = pd.melt(test, value_vars=df.columns, ignore_index=False,\n",
    "                var_name='unique_id', value_name='y')\n",
    "\n",
    "train['unique_id'] = train['unique_id'].str.replace('ts_','').astype(int)\n",
    "test['unique_id'] = test['unique_id'].str.replace('ts_','').astype(int)\n",
    "\n",
    "train.groupby('unique_id').tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6628e685",
   "metadata": {
    "papermill": {
     "duration": 0.011167,
     "end_time": "2024-04-02T13:23:10.998401",
     "exception": false,
     "start_time": "2024-04-02T13:23:10.987234",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## FIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0cbdb6d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T13:23:11.023578Z",
     "iopub.status.busy": "2024-04-02T13:23:11.022955Z",
     "iopub.status.idle": "2024-04-02T13:23:11.029301Z",
     "shell.execute_reply": "2024-04-02T13:23:11.027930Z"
    },
    "papermill": {
     "duration": 0.02189,
     "end_time": "2024-04-02T13:23:11.032086",
     "exception": false,
     "start_time": "2024-04-02T13:23:11.010196",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### INITIALIZE A FORECASTING MODEL INSTANCE ###\n",
    "\n",
    "forecaster = ForecastingCascade(\n",
    "    Ridge(),\n",
    "    lags=range(1,24*7+1),\n",
    "    groups=[0],\n",
    "    target_diff=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9dded217",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T13:23:11.056868Z",
     "iopub.status.busy": "2024-04-02T13:23:11.056441Z",
     "iopub.status.idle": "2024-04-02T13:23:11.572243Z",
     "shell.execute_reply": "2024-04-02T13:23:11.570474Z"
    },
    "papermill": {
     "duration": 0.53309,
     "end_time": "2024-04-02T13:23:11.576818",
     "exception": false,
     "start_time": "2024-04-02T13:23:11.043728",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ForecastingCascade(estimator=Ridge(), groups=[0], lags=range(1, 169),\n",
       "                   target_diff=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ForecastingCascade</label><div class=\"sk-toggleable__content\"><pre>ForecastingCascade(estimator=Ridge(), groups=[0], lags=range(1, 169),\n",
       "                   target_diff=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ForecastingCascade(estimator=Ridge(), groups=[0], lags=range(1, 169),\n",
       "                   target_diff=True)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### FIT ###\n",
    "\n",
    "forecaster.fit(X=train[['unique_id']], y=train['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4b6e909",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T13:23:11.666510Z",
     "iopub.status.busy": "2024-04-02T13:23:11.666101Z",
     "iopub.status.idle": "2024-04-02T13:23:11.672290Z",
     "shell.execute_reply": "2024-04-02T13:23:11.671369Z"
    },
    "papermill": {
     "duration": 0.039901,
     "end_time": "2024-04-02T13:23:11.674484",
     "exception": false,
     "start_time": "2024-04-02T13:23:11.634583",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### SAVE AND LOAD A FITTED MODEL INSTANCE ###\n",
    "\n",
    "import pickle\n",
    "\n",
    "# save\n",
    "pickle.dump(forecaster, open('model.pkl','wb'))\n",
    "\n",
    "# load\n",
    "forecaster = pickle.load(open('model.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dabec6c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T13:23:11.701301Z",
     "iopub.status.busy": "2024-04-02T13:23:11.700509Z",
     "iopub.status.idle": "2024-04-02T13:23:12.685874Z",
     "shell.execute_reply": "2024-04-02T13:23:12.684586Z"
    },
    "papermill": {
     "duration": 1.002445,
     "end_time": "2024-04-02T13:23:12.688815",
     "exception": false,
     "start_time": "2024-04-02T13:23:11.686370",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 2 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "83b5a172647d4e2983c12d45f9b26ed0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=5), Label(value='0 / 5'))), HBox(c…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PARALLEL MODELS FIT FOR EACH TIME SERIES ###\n",
    "\n",
    "from pandarallel import pandarallel\n",
    "pandarallel.initialize(progress_bar=True)\n",
    "\n",
    "models = train.groupby('unique_id').parallel_apply(\n",
    "    lambda x: forecaster.fit(X=x[['unique_id']], y=x['y'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9964acbb",
   "metadata": {
    "papermill": {
     "duration": 0.011618,
     "end_time": "2024-04-02T13:23:12.713040",
     "exception": false,
     "start_time": "2024-04-02T13:23:12.701422",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## INFERENCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5cf981a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T13:23:12.741353Z",
     "iopub.status.busy": "2024-04-02T13:23:12.740295Z",
     "iopub.status.idle": "2024-04-02T13:23:13.260675Z",
     "shell.execute_reply": "2024-04-02T13:23:13.259765Z"
    },
    "papermill": {
     "duration": 0.537199,
     "end_time": "2024-04-02T13:23:13.263378",
     "exception": false,
     "start_time": "2024-04-02T13:23:12.726179",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### FORECASTING ###\n",
    "\n",
    "preds = forecaster.predict(X=test[['unique_id']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d703a321",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T13:23:13.289655Z",
     "iopub.status.busy": "2024-04-02T13:23:13.288583Z",
     "iopub.status.idle": "2024-04-02T13:23:14.627968Z",
     "shell.execute_reply": "2024-04-02T13:23:14.624645Z"
    },
    "papermill": {
     "duration": 1.355865,
     "end_time": "2024-04-02T13:23:14.631116",
     "exception": false,
     "start_time": "2024-04-02T13:23:13.275251",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Pandarallel will run on 2 workers.\n",
      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f08cedfc5ed841fba037d9217f51558a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(IntProgress(value=0, description='0.00%', max=5), Label(value='0 / 5'))), HBox(c…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### PARALLEL MODELS FORECASTING FOR EACH TIME SERIES ###\n",
    "\n",
    "from pandarallel import pandarallel\n",
    "pandarallel.initialize(progress_bar=True)\n",
    "\n",
    "preds = train.groupby('unique_id').parallel_apply(\n",
    "    lambda x: forecaster.fit(\n",
    "        X=x[['unique_id']], y=x['y']\n",
    "    ).predict(\n",
    "        X=pd.DataFrame({\n",
    "            'unique_id': np.full((24*7,), x['unique_id'].iloc[0])\n",
    "        })\n",
    "    )\n",
    ").explode().to_frame('pred').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "db87b1fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T13:23:14.658627Z",
     "iopub.status.busy": "2024-04-02T13:23:14.657769Z",
     "iopub.status.idle": "2024-04-02T13:23:15.184240Z",
     "shell.execute_reply": "2024-04-02T13:23:15.182598Z"
    },
    "papermill": {
     "duration": 0.543754,
     "end_time": "2024-04-02T13:23:15.187185",
     "exception": false,
     "start_time": "2024-04-02T13:23:14.643431",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "### FORECASTING PASSING NEW DATA ###\n",
    "\n",
    "preds = forecaster.predict(\n",
    "    X=pd.DataFrame({'unique_id': \n",
    "                    np.repeat(train['unique_id'].unique(), 24*7)}),\n",
    "    last_y=train.groupby('unique_id')['y'].head(24*8),\n",
    "    last_X=train.groupby('unique_id')[['unique_id']].head(24*8)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d283dca3",
   "metadata": {
    "papermill": {
     "duration": 0.012736,
     "end_time": "2024-04-02T13:23:15.212461",
     "exception": false,
     "start_time": "2024-04-02T13:23:15.199725",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## EVALUATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ef14b23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T13:23:15.240005Z",
     "iopub.status.busy": "2024-04-02T13:23:15.239537Z",
     "iopub.status.idle": "2024-04-02T13:23:17.672730Z",
     "shell.execute_reply": "2024-04-02T13:23:17.671850Z"
    },
    "papermill": {
     "duration": 2.449696,
     "end_time": "2024-04-02T13:23:17.675266",
     "exception": false,
     "start_time": "2024-04-02T13:23:15.225570",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-38.29185546, -38.06164873, -36.33975979, -39.41807432,\n",
       "       -34.08109687])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### CROSS-VALITION SCORING ###\n",
    "\n",
    "cv_scores = cross_val_score(\n",
    "    forecaster,\n",
    "    X=train[['unique_id']], y=train['y'],\n",
    "    groups=train[['unique_id']],\n",
    "    cv=TemporalSplit(n_splits=5, test_size=24*2, gap=24*2),\n",
    "    scoring='neg_mean_squared_error', error_score='raise',\n",
    ")\n",
    "\n",
    "cv_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5920b43b",
   "metadata": {
    "papermill": {
     "duration": 0.012117,
     "end_time": "2024-04-02T13:23:17.699899",
     "exception": false,
     "start_time": "2024-04-02T13:23:17.687782",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## PARAMETER TUNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e5d10675",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-02T13:23:17.727479Z",
     "iopub.status.busy": "2024-04-02T13:23:17.727000Z",
     "iopub.status.idle": "2024-04-02T13:23:47.351994Z",
     "shell.execute_reply": "2024-04-02T13:23:47.350331Z"
    },
    "papermill": {
     "duration": 29.644283,
     "end_time": "2024-04-02T13:23:47.356755",
     "exception": false,
     "start_time": "2024-04-02T13:23:17.712472",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-36.612980016769626, OrderedDict([('estimator__alpha', 1.0109347810455676)]))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### BAYESIAN PARAMETER TUNING ###\n",
    "\n",
    "model = skopt.BayesSearchCV( \n",
    "    forecaster,\n",
    "    search_spaces={\n",
    "        'estimator__alpha': skopt.space.Real(1,20, prior='log-uniform'),\n",
    "    },\n",
    "    cv=TemporalSplit(n_splits=3, test_size=24*2, gap=24*2),\n",
    "    scoring='neg_mean_squared_error', error_score='raise',\n",
    "    n_iter=15, random_state=42, refit=True,\n",
    ").fit(train[['unique_id']], train['y'], groups=train[['unique_id']])\n",
    "\n",
    "model.best_score_, model.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06976a63",
   "metadata": {
    "papermill": {
     "duration": 0.041827,
     "end_time": "2024-04-02T13:23:47.457945",
     "exception": false,
     "start_time": "2024-04-02T13:23:47.416118",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## FEATURE SELECTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e6066541",
   "metadata": {
    "execution": {
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   "source": [
    "### FEATURE SELECTION WITH RECURSIVE STRATEGY ###\n",
    "\n",
    "forecaster = ForecastingCascade(\n",
    "    RFECV(\n",
    "        Ridge(),\n",
    "        cv=ShuffleSplit(1, test_size=0.25),\n",
    "        scoring='neg_mean_squared_error',\n",
    "    ),\n",
    "    lags=range(1,24*7+1),\n",
    "    groups=[0],\n",
    "    target_diff=True,\n",
    ").fit(train[['unique_id']], train['y'])\n",
    "\n",
    "\n",
    "feature_ranking = pd.DataFrame({\n",
    "    'feature': forecaster.feature_names_,\n",
    "    'ranking': forecaster.estimator_.ranking_,\n",
    "}).sort_values(by='ranking')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "049c5057",
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     "start_time": "2024-04-02T13:24:17.206939",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## EXPLAINABILITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0e229b76",
   "metadata": {
    "execution": {
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   "source": [
    "### TRAINING WITH PERMUTATION IMPORTANCE ###\n",
    "\n",
    "class ExplainerRegressor(Ridge):\n",
    "    def fit(self, X,y):\n",
    "        id_train, id_val = train_test_split(\n",
    "            np.arange(X.shape[0]), random_state=42,\n",
    "        )\n",
    "        super().fit(X[id_train], y[id_train])\n",
    "        \n",
    "        self.perm_feature_importance_ = permutation_importance(\n",
    "            self, X[id_val], y[id_val], \n",
    "            n_repeats=5, random_state=42,\n",
    "        )['importances_mean']\n",
    "        \n",
    "        return super().fit(X, y)\n",
    "    \n",
    "forecaster = ForecastingCascade(\n",
    "    ExplainerRegressor(),\n",
    "    lags=range(1,24*7+1),\n",
    "    groups=[0],\n",
    "    target_diff=True,\n",
    ").fit(train[['unique_id']], train['y'])\n",
    "\n",
    "\n",
    "feature_importance = pd.DataFrame({\n",
    "    'feature': forecaster.feature_names_,\n",
    "    'importance': forecaster.estimator_.perm_feature_importance_,\n",
    "}).sort_values(by='importance')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d187e4a",
   "metadata": {
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     "duration": 0.054854,
     "end_time": "2024-04-02T13:24:25.023446",
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     "start_time": "2024-04-02T13:24:24.968592",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## PERFORMANCE DRIFT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "92204a46",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.4, pvalue=0.41752365281777043, statistic_location=33.499850949938875, statistic_sign=1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### SEARCHING FOR PERFORMCE DRIFTS ###\n",
    "\n",
    "CV = TemporalSplit(n_splits=20, test_size=24, gap=24)\n",
    "stream_simulator = CV.split(train, groups=train['unique_id'])\n",
    "\n",
    "scores_valid, scores_test = [], []\n",
    "for i, (id_past, id_future) in enumerate(stream_simulator):\n",
    "    if i < 1:\n",
    "        forecaster.fit(\n",
    "            X=train[['unique_id']].iloc[id_past], \n",
    "            y=train['y'].iloc[id_past]\n",
    "        )\n",
    "    score = forecaster.score(\n",
    "        X=train[['unique_id']].iloc[id_future], \n",
    "        y=train['y'].iloc[id_future],\n",
    "        last_X=train[['unique_id']].iloc[id_past],\n",
    "        last_y=train['y'].iloc[id_past],\n",
    "        scoring='mse'\n",
    "    )\n",
    "    if i < CV.get_n_splits() //2:\n",
    "        scores_valid.append(score)\n",
    "    else:\n",
    "        scores_test.append(score)\n",
    "        \n",
    "        \n",
    "ks_2samp(scores_valid, scores_test)"
   ]
  }
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